{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5hIbr52I7Z7U"
   },
   "source": [
    "First Step on Deep Learning Using Tensorflow\n",
    "=============\n",
    "\n",
    "\n",
    "各位同学们，我们在第九课上已经给大家介绍过了基础的深度学习知识。 这种最简单的网络是上一层网络全部链接到下一层网络，所以也称作 fully-connected（全连接） 网络。 \n",
    "\n",
    "在本次作业中，我们将使用经典数据集 mnist的升级版，notMNIST构建神经网络进行图像分类。 \n",
    "\n",
    "本次作业，你需要完成1, 2, 3三个联系。 能够掌握：\n",
    "\n",
    "1. 数据预处理的知识\n",
    "2. 深度学习程序的构建方式\n",
    "3. 训练集、测试集、验证集\n",
    "4. 神经网络的基础知识\n",
    "5. 图计算网络\n",
    "6. 正则化\n",
    "\n",
    "等非常重要的知识 \n",
    "\n",
    "Assignment 1\n",
    "------------\n",
    "\n",
    "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
    "\n",
    "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.\n",
    "\n",
    "# The Assignment mainly take references from the following github:\n",
    "https://github.com/rndbrtrnd/udacity-deep-learning/blob/master/1_notmnist.ipynb\n",
    "https://github.com/hankcs/udacity-deep-learning/blob/master/1_notmnist.py#L389"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "apJbCsBHl-2A"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "import random\n",
    "from IPython.display import display, Image\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNWGtZaXn-5j"
   },
   "source": [
    "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labeled examples. Given these sizes, it should be possible to train models quickly on any machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 186058,
     "status": "ok",
     "timestamp": 1444485672507,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "EYRJ4ICW6-da",
    "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "url = 'https://commondatastorage.googleapis.com/books1000/'\n",
    "last_percent_reported = None\n",
    "data_root = '.' # Change me to store data elsewhere\n",
    "\n",
    "def download_progress_hook(count, blockSize, totalSize):\n",
    "  \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
    "  slow internet connections. Reports every 5% change in download progress.\n",
    "  \"\"\"\n",
    "  global last_percent_reported\n",
    "  percent = int(count * blockSize * 100 / totalSize)\n",
    "\n",
    "  if last_percent_reported != percent:\n",
    "    if percent % 5 == 0:\n",
    "      sys.stdout.write(\"%s%%\" % percent)\n",
    "      sys.stdout.flush()\n",
    "    else:\n",
    "      sys.stdout.write(\".\")\n",
    "      sys.stdout.flush()\n",
    "      \n",
    "    last_percent_reported = percent\n",
    "        \n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  dest_filename = os.path.join(data_root, filename)\n",
    "  if force or not os.path.exists(dest_filename):\n",
    "    print('Attempting to download:', filename) \n",
    "    filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)\n",
    "    print('\\nDownload Complete!')\n",
    "  statinfo = os.stat(dest_filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified', dest_filename)\n",
    "  else:\n",
    "    raise Exception(\n",
    "      'Failed to verify ' + dest_filename + '. Can you get to it with a browser?')\n",
    "  return dest_filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)\n",
    "\n",
    "'''\n",
    "\n",
    "'''\n",
    "url = http://yaroslavvb.com/upload/notMNIST/\n",
    "'''\n",
    "data_root = r'D:\\GitHub\\Data\\notMNIST'\n",
    "train_filename = r'D:\\GitHub\\Data\\notMNIST\\notMNIST_large.tar.gz'\n",
    "test_filename = r'D:\\GitHub\\Data\\notMNIST\\notMNIST_small.tar.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cC3p0oEyF8QT"
   },
   "source": [
    "Extract the dataset from the compressed .tar.gz file.\n",
    "This should give you a set of directories, labeled A through J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 186055,
     "status": "ok",
     "timestamp": 1444485672525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "H8CBE-WZ8nmj",
    "outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data for D:\\GitHub\\Data\\notMNIST\\notMNIST_large. This may take a while. Please wait.\n",
      "['D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\A', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\B', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\C', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\D', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\E', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\F', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\G', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\H', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\I', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_large\\\\J']\n",
      "Extracting data for D:\\GitHub\\Data\\notMNIST\\notMNIST_small. This may take a while. Please wait.\n",
      "['D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\A', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\B', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\C', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\D', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\E', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\F', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\G', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\H', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\I', 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "    root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "    if os.path.isdir(root) and not force:\n",
    "    # You may override by setting force=True.\n",
    "        print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "    else:\n",
    "        print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "        tar = tarfile.open(filename)\n",
    "        sys.stdout.flush()\n",
    "        tar.extractall(data_root)\n",
    "        tar.close()\n",
    "    data_folders = [\n",
    "        os.path.join(root, d) for d in sorted(os.listdir(root))\n",
    "        if os.path.isdir(os.path.join(root, d))]\n",
    "    if len(data_folders) != num_classes:\n",
    "        raise Exception(\n",
    "          'Expected %d folders, one per class. Found %d instead.' % (\n",
    "            num_classes, len(data_folders)))\n",
    "    print(data_folders)\n",
    "    return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4riXK3IoHgx6"
   },
   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\B\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\C\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\D\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\E\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\F\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\G\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\H\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\I\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\J\n"
     ]
    }
   ],
   "source": [
    "for f in train_folders:\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "# use IPython display images\n",
    "def image_sample(data_folders, sample_size):\n",
    "    for folder in data_folders:\n",
    "        image_files = os.listdir(folder)\n",
    "        image_samples = random.sample(image_files, sample_size)\n",
    "        for image in image_samples:\n",
    "            image_file = os.path.join(folder, image)\n",
    "            display(Image(image_file))\n",
    "            \n",
    "#image_sample(train_folders, 10)\n",
    "\n",
    "# use matplot to plot images\n",
    "def plot_sample(data_folders, sample_size, title=None):\n",
    "    fig = plt.figure()\n",
    "    \n",
    "    if title: fig.suptitle(title, fontsize=16, fontweight='bold')\n",
    "    \n",
    "    for folder in data_folders:\n",
    "        image_files = os.listdir(folder)\n",
    "        image_sample = random.sample(image_files, sample_size)\n",
    "        for image in image_sample:\n",
    "            image_file = os.path.join(folder, image)\n",
    "            ax = fig.add_subplot(len(data_folders), \n",
    "                                 sample_size,\n",
    "                                 sample_size * data_folders.index(folder) + image_sample.index(image) + 1)\n",
    "            image = mpimg.imread(image_file)\n",
    "            ax.imshow(image)\n",
    "            ax.set_axis_off()\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "plot_sample(train_folders, 10, 'Train_Folders')\n",
    "plot_sample(test_folders, 10, 'Test_Folders')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PBdkjESPK8tw"
   },
   "source": [
    "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
    "\n",
    "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
    "\n",
    "A few images might not be readable, we'll just skip them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 30
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 399874,
     "status": "ok",
     "timestamp": 1444485886378,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "h7q0XhG3MJdf",
    "outputId": "92c391bb-86ff-431d-9ada-315568a19e59",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A\\RnJlaWdodERpc3BCb29rSXRhbGljLnR0Zg==.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A\\SG90IE11c3RhcmQgQlROIFBvc3Rlci50dGY=.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A\\Um9tYW5hIEJvbGQucGZi.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Full dataset tensor: (52909, 28, 28)\n",
      "Mean: -0.12825024\n",
      "Standard deviation: 0.44312063\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\B.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\B\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\B\\TmlraXNFRi1TZW1pQm9sZEl0YWxpYy5vdGY=.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Full dataset tensor: (52911, 28, 28)\n",
      "Mean: -0.0075630303\n",
      "Standard deviation: 0.45449105\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\C.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\C\n",
      "Full dataset tensor: (52912, 28, 28)\n",
      "Mean: -0.14225811\n",
      "Standard deviation: 0.43980625\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\D.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\D\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\D\\VHJhbnNpdCBCb2xkLnR0Zg==.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Full dataset tensor: (52911, 28, 28)\n",
      "Mean: -0.057367794\n",
      "Standard deviation: 0.45564765\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\E.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\E\n",
      "Full dataset tensor: (52912, 28, 28)\n",
      "Mean: -0.06989899\n",
      "Standard deviation: 0.45294195\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\F.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\F\n",
      "Full dataset tensor: (52912, 28, 28)\n",
      "Mean: -0.1255833\n",
      "Standard deviation: 0.44708964\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\G.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\G\n",
      "Full dataset tensor: (52912, 28, 28)\n",
      "Mean: -0.09458135\n",
      "Standard deviation: 0.44623983\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\H.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\H\n",
      "Full dataset tensor: (52912, 28, 28)\n",
      "Mean: -0.06852206\n",
      "Standard deviation: 0.45423177\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\I.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\I\n",
      "Full dataset tensor: (52912, 28, 28)\n",
      "Mean: 0.03078625\n",
      "Standard deviation: 0.46889907\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\J.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\J\n",
      "Full dataset tensor: (52911, 28, 28)\n",
      "Mean: -0.15335836\n",
      "Standard deviation: 0.44365644\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\A.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\A\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\A\\RGVtb2NyYXRpY2FCb2xkT2xkc3R5bGUgQm9sZC50dGY=.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Full dataset tensor: (1872, 28, 28)\n",
      "Mean: -0.13262637\n",
      "Standard deviation: 0.44512793\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\B.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\B\n",
      "Full dataset tensor: (1873, 28, 28)\n",
      "Mean: 0.005356085\n",
      "Standard deviation: 0.45711532\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\C.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\C\n",
      "Full dataset tensor: (1873, 28, 28)\n",
      "Mean: -0.1415206\n",
      "Standard deviation: 0.4426903\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\D.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\D\n",
      "Full dataset tensor: (1873, 28, 28)\n",
      "Mean: -0.04921666\n",
      "Standard deviation: 0.4597589\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\E.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\E\n",
      "Full dataset tensor: (1873, 28, 28)\n",
      "Mean: -0.05991479\n",
      "Standard deviation: 0.45734963\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\F.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\F\n",
      "Could not read: D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\F\\Q3Jvc3NvdmVyIEJvbGRPYmxpcXVlLnR0Zg==.png : Could not find a format to read the specified file in mode 'i' - it's ok, skipping.\n",
      "Full dataset tensor: (1872, 28, 28)\n",
      "Mean: -0.118185304\n",
      "Standard deviation: 0.45227867\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\G.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\G\n",
      "Full dataset tensor: (1872, 28, 28)\n",
      "Mean: -0.09255028\n",
      "Standard deviation: 0.44900584\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\H.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\H\n",
      "Full dataset tensor: (1872, 28, 28)\n",
      "Mean: -0.05868925\n",
      "Standard deviation: 0.45875895\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\I.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\I\n",
      "Full dataset tensor: (1872, 28, 28)\n",
      "Mean: 0.05264507\n",
      "Standard deviation: 0.47189355\n",
      "Pickling D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\J.pickle.\n",
      "D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\J\n",
      "Full dataset tensor: (1872, 28, 28)\n",
      "Mean: -0.15168911\n",
      "Standard deviation: 0.44801357\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "    \"\"\"Load the data for a single letter label.\"\"\"\n",
    "    image_files = os.listdir(folder)\n",
    "    dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
    "                         dtype=np.float32)\n",
    "    print(folder)\n",
    "    num_images = 0\n",
    "    for image in image_files:\n",
    "        image_file = os.path.join(folder, image)\n",
    "        try:\n",
    "            image_data = (imageio.imread(image_file).astype(float) - pixel_depth / 2) / pixel_depth\n",
    "            if image_data.shape != (image_size, image_size):\n",
    "                raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "            dataset[num_images, :, :] = image_data\n",
    "            num_images = num_images + 1\n",
    "        except (IOError, ValueError) as e:\n",
    "            print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "    dataset = dataset[0:num_images, :, :]\n",
    "    if num_images < min_num_images:\n",
    "        raise Exception('Many fewer images than expected: %d < %d' %(num_images, min_num_images))\n",
    "    \n",
    "    print('Full dataset tensor:', dataset.shape)\n",
    "    print('Mean:', np.mean(dataset))\n",
    "    print('Standard deviation:', np.std(dataset))\n",
    "    return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "    dataset_names = []\n",
    "    for folder in data_folders:\n",
    "        set_filename = folder + '.pickle'\n",
    "        dataset_names.append(set_filename)\n",
    "        if os.path.exists(set_filename) and not force:\n",
    "            # You may override by setting force=True.\n",
    "            print('%s already present - Skipping pickling.' % set_filename)\n",
    "        else:\n",
    "            print('Pickling %s.' % set_filename)\n",
    "            dataset = load_letter(folder, min_num_images_per_class)\n",
    "            try:\n",
    "                with open(set_filename, 'wb') as f:\n",
    "                    pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "            except Exception as e:\n",
    "                print('Unable to save data to', set_filename, ':', e)\n",
    "\n",
    "    return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_datasets\n",
    "\n",
    "# read pickle file\n",
    "def read_pickle_file(path):\n",
    "    datasets = []\n",
    "    files = os.listdir(path)\n",
    "    for file in files:\n",
    "        if 'pickle' in file:\n",
    "            datasets.append(path + '\\\\' + file)\n",
    "    return datasets\n",
    "\n",
    "pickle_train_datasets = read_pickle_file(r'D:\\GitHub\\Data\\notMNIST\\notMNIST_large')\n",
    "pickle_test_datasets = read_pickle_file(r'D:\\GitHub\\Data\\notMNIST\\notMNIST_small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\A.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\B.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\C.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\D.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\E.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\F.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\G.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\H.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\I.pickle',\n",
       " 'D:\\\\GitHub\\\\Data\\\\notMNIST\\\\notMNIST_small\\\\J.pickle']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_test_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vUdbskYE2d87"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate fake labels\n",
    "def generate_labels(sizes):\n",
    "    labels = np.ndarray(sum(sizes), dtype=np.int32)\n",
    "    start = 0\n",
    "    end = 0\n",
    "    for label, size in enumerate(sizes):\n",
    "        start = end\n",
    "        end += size\n",
    "        for i in range(start, end):\n",
    "            labels[i] = label\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subplot(nrows, ncols, index, **kwargs)\n",
    "\n",
    "def display_sample(dataset, sample_size, title=None):\n",
    "    fig = plt.figure()\n",
    "    \n",
    "    if title: fig.suptitle(title)\n",
    "    num_of_images = []\n",
    "    for pickle_file in dataset:\n",
    "        with open(pickle_file, 'rb') as f:\n",
    "            data = pickle.load(f)\n",
    "            print('Total images in ', pickle_file, ': ', len(data))\n",
    "            \n",
    "            for index, image in enumerate(data):\n",
    "                if index == sample_size: break\n",
    "                ax = fig.add_subplot(len(dataset), sample_size, sample_size * dataset.index(pickle_file) + index + 1)\n",
    "                ax.imshow(image)\n",
    "                ax.set_axis_off()\n",
    "                ax.imshow(image)\n",
    "            num_of_images.append(len(data))\n",
    "    \n",
    "    # add check balance\n",
    "    check_balance(num_of_images)\n",
    "    plt.show()\n",
    "    return num_of_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\A.pickle :  52909\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\B.pickle :  52911\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\C.pickle :  52912\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\D.pickle :  52911\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\E.pickle :  52912\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\F.pickle :  52912\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\G.pickle :  52912\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\H.pickle :  52912\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\I.pickle :  52912\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_large\\J.pickle :  52911\n",
      "mean of images:  52911.4\n",
      "Well balanced  52909\n",
      "Well balanced  52911\n",
      "Well balanced  52912\n",
      "Well balanced  52911\n",
      "Well balanced  52912\n",
      "Well balanced  52912\n",
      "Well balanced  52912\n",
      "Well balanced  52912\n",
      "Well balanced  52912\n",
      "Well balanced  52911\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\A.pickle :  1872\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\B.pickle :  1873\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\C.pickle :  1873\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\D.pickle :  1873\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\E.pickle :  1873\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\F.pickle :  1872\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\G.pickle :  1872\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\H.pickle :  1872\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\I.pickle :  1872\n",
      "Total images in  D:\\GitHub\\Data\\notMNIST\\notMNIST_small\\J.pickle :  1872\n",
      "mean of images:  1872.4\n",
      "Well balanced  1872\n",
      "Well balanced  1873\n",
      "Well balanced  1873\n",
      "Well balanced  1873\n",
      "Well balanced  1873\n",
      "Well balanced  1872\n",
      "Well balanced  1872\n",
      "Well balanced  1872\n",
      "Well balanced  1872\n",
      "Well balanced  1872\n"
     ]
    },
    {
     "data": {
      "image/png": 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o7pFR13bsv31m5RXRNXp+spaFRVFG0RYLSY+vIdUajRIbW6kJzFcl4RteC/e/y1PF0Yiw2jjyZDcW/zzXqyipQqFXZJD7K4T3/Tg1xcKpz+ty+qv6wX9zajHz3un8XOxv990zvRl7pjdj97Rkyq5ui7DaUJs1Yfd7ycb295JRmzVBWG2UXd2W3dOSvef8N6mSCiEsFo5dXYuhcRuwihiW/diJJlM2sE/pyDf1q3FLzHlebRlL9Q3hX45KTQeVoWs88cXnaFJHR5L+6njqRWUGDKH17mkoq7cFFb7CYiFnUjqQSda11UDkeW18s/LrMSLuOEqbFug794YU3jV2nke6J75ilAgaL9ZBSpQ6pV4h/srgoRx5Mo5Gb2T4CV9htZH9dEeaPLcaz6PaNyENCxvRgHpZhgaOlGjn8lnwzJvc/1HwYeRPV7wPRPPZ2a7gLk3LP+/d/1qfucwiMXR/SoleUEBxnzJq6/l+u04rlb+Q8Wq0ryhnGcnDNjOIdO+2ZDZT/nUcXf0o31I7ZHndlh3mj8tOogMpD6zjqpRBrGgzH9GxDXLjzso/puWRklYri1h7eQTCbjzmihDYlTAfVDeVTYRVhrKqkuc8Y1vofVIiHQ7avTmWeudXIzq2wXLsLK6jubh6pmHfko1cs4X2L4yhNmHMFG4er7WXvg07gNCRLhfCauPyV8YS900u9n7FIP37dNHRTbx6phX9dl8PHDXaUzse3eqetLa4/51zBIAe2wfS7eNtvFgnfJuF1YajVzt2PDAdh3RySnPQ0BLakCNUlZLr2qPJDH5L+zzkcR50dNqtvIcUq09L3dLDGPUV6C76r3uMeqstuOpUY2PP97AKBafUue2DsahHLeSl2vmz5xvEKh6R+GSl17xYqqTpSpeLdx+dTqI1hi8KatHkh3yEzUrjH85wTeRxnFLDdesZLElNw5bjkM5KfyERAiUqih6ROqpQsAqVOu+tRi91IKw2btrbz1tGrdcPB0yWlG/LjvHTyddL0M6c9Q7nhMXCnEk34ZBOBsz7I/gLLgSWxCbIjTu8X/e+CWnYF61HbtxB0hD3g6dryI072PFAoNYsnWV07et76UIOnYUARWXEsPE4r+0YoO26rulAM4thU1ye09y3Q9eYcz4egFtj8hGXtw7ZpQAoKrJdKlT46e1bhD8vRFnCavP+LmjkIyXbCxL8NkXdcBSrUBny2eILE7iKSsO1MWzvoKNE2BGq6tW67UrlHgpZUzrw+sF1TDv0Z9DfO9khtHU3e+e0BwwzgseUoNaoQVm/TiAEWdPTg5/o1u7U6nFsmTQdtVZNBs75FfVLHaVdSx7/YA45H9XFcX0nNj07w9vWoEVZLJwZ0ZUvCmp5J0QBpMtJnfdWs7LNd0H71CFd/Ph6D8SN57zbflr3I5Y252n2yz24kkv4ad2P3n2Rg07xy+RuOGQYbw2rDcc1l/HrJx/hlBp2YWVEY39FQm2e7N8WoZAz2MV5vRS9Cv9FChsN5lv93vtIYSNS2IhSVK+kkwKiFKt7uxWvmViBKEX1nvPfpEqarto8mZnHanBV4q98fbwTcuMOJKCeOM0V60aypetsNnaYR/+CPmHLqarbRzCEqnJsRBpO+QdWUe7m6Boogh2bm0IyWFB5ueFCxsrwkww3776NiKb4hkZCELPjBHZhDa2RCYW8zvWJPXjIt8luN4ZbinFX9eJi776DzsKg116Z2ZLmlk0+W2G3duy7PYKohoU4nSoNZtmwLtsIuoby22aOLWhNw1/9X678JJvXzJKfF02dcvu2FDWGaqcBKEiOIWZziE5wT6Qt+u6zoLurMvHph66FnZwJiRBY6tZhQv1F7NjVAA0FFR2nVNGkzvBqp/mChhdUj735tYlUStCLiw3XHvfbZVF0KpuKPnDLB4Dd/at6GzzPUsrwTQB+rpRaXh62JesBSB2b4TuvgtCUmoZ2Lp/u40cRfWYdP1zXHpmXj37+OO/0v4kGB/chnWX0uO9+7KwP6TUidYk+4CybCpugREeixNfEdSjHzxSg1qqJdjYvwDygaIbZqTz1485TEBHYH7LMiVKZNc9ZxpSZ7+OUFqxCJXHpCFLZyDFXIXVUw/RR0KoW0QcO+/VD95R91HDvrwox20+h6eXa534/4kQk0qNeCuGVQyqKt++kgtcd7L9NlYTuiR61+aHpPEBhctNv+NefN5BbFEen+EO8UmMRClYK9VLy+jSj2peh3bb+iveCdLnIfGI65T0W/I/dABid2cwafNgirDb2vtEeh1zPilYL0f4Mpg0bd8fVqwOWXzP9tQFdM25euRdMqVaNkwOTKYkX2AqgznurDY1FVRn6+KMUPaxQf5rvJVPataTFIzvQ3Rrw0txMEpd2oPnoTGPixM3BV7uS+JQxkbY2fRa3OLv41fJMN99LYT/o/zIsPtiKt+obL/6xKyHlP0G7w4+rxo4kesUucDrBamVQxv5KzznoLCTR3dfCamPvJ63Z1vMDohQbxXoZbX8dRcq9O7yTNm+fTQpZVrMfzzL2zQeQQiCkNMw3gHPcT4yufgAlrRX61j1V0ni7bHHy1c5qRCwwtEwBPNviJ6yoRChOkBf/8Q+FflUaym+hvm6hKRjscx+zr6zLqAYrmZrcgpjvNyIB1+Ej3v3avmxv+yN+Nkw3i45uosUX44JUSGNTx6/RpM76bZIuEf7CvVgvw7bASkmPyuvYNyENhRziPP+mih9j93uy6OgmdAyBe337vqQe3wjAh3np/DN+CwB5wwqJ+q6coNc1pjVaStprj1B3aviRhRpfi0VbV6DtO+hf7waXe/+uK1ejA8ofm/22K3IzOlB36mr6TvNtX/bXPRVDUqnQFVYbL0z6t/erUVdV+DLxV69d1SrsXheh255aws+LE9Hy8v7WSgqLhcKBHdDkJlSheK+3o6yETIehAQ2NPePd7pQap0d1pfas9X7lSGcZ+2+biUc4pz8/Ds3u0xIsxZINL85AkzqvfjyTZxID/Slr/nkUz0Bq1uFV1FejmH4uiykr+lEWpyAWNqfuHUfQi4uJnbuW1bmZ9J3ie0h3j4shdWQRwmqj8KbLmXRckHrPRq+Q8ZD4xBoc13cicuVOOs6aSFKqz/9QWCzccblPkMft8/94uPbGgtvVsEuHLM5WwUapOCWy1IF0ORGajh7CPcdD9ktdGXNza5b88EXY48qzLL0e2S+2C9whJXbFRZ1PNkE5TUWoCouXd2Tsrwe54cs/WNg6Pmz5wmrj+JiOZHTeRJLc7S1L2Ky8MrcfvS+fjV248Nz/UFyMcqD8sbXSc4JRbcEm79/abZJpjQaD2O67X+W10HIfHOlygRBc/eAYkr5ZA+WdGRQVNSURyKR/g/aBF1VUXD3TWPHZLPrKCxzNVBVFRSiCJYc3oEnDC6HtO2Op17wEkVQPLdJCfdtiFAxTYUb6vxkk/c0ucUokCYuOobkXHwVDWG0cGN8cWBG4z+YzE0hnOc+TYBN/IRYJeZ6Fxqzm1n91LVe4MJQJIeibkIaNQ975i0ENjXYsCzFnW6nQPTGqI90jfkOTdpKXjuS6ttvJ+ND4Ipzp6qTOSis3Pvorw6tvYHz1Ayxt3BXKTeb8HUiXi9/eneH9d5cXHiD+w7V+nTdHNCb30a5kTngPBcHqZ6dy0wc+oSksFopuNAS3C412sx4k8fMtxs3wHKMq9Lx9AEtazSfdbkWJikIvKfHuV1OScO09gLBYKLmuPQ0tmVzfqT+u3GOkWnxazr6XOpD0ePBJjmp1DJODdJaxatoH9LnjHhRlq78N2j3kFBqIhLo0fWUjh75M9u7OmtKBn+rMwKOVV99d4DdhVX2P7+8Pmyzm8lceJumxyiddkLrbxq2jVWLujzwhUI/n0fLPYTQmzMSQm9Q5Y2jY3Unj5aXwlG+7sFg4PiadHdfuQZad9bun0glk7UcVCuOq57BQhneBk84yzqdq1NP0AJcoj3khQnECf/8wUqjh/V5DnlduyH5oejxDkjP47bIq1k9KvpzyFv1THgvYlfeOJPX34SQqOwIFlq5hWbGRHWUlZE1Pp/mDm8J+lJfmZqJJ3at4ndaKGNroitBtslhQqsexaKshCFWhoEmdbROm+60ANZQ241mPUoLbUStqrxWRLie9+29kZUng85r1piEwhSZIWuBA+XNrcOGtqOhXXMaBm+1ItWqeGM/u30hHu8b3RfHMSg0zWR2EsG+WWqsmBd2KiRF2djsdJH4p2d+plFofr6XWx2tJHbGR6p9nsLpPY5zS6Nw9D0ch1L/XdUxp19Lvhsd/sCbwayUlCW+t446DfdwrXaxY6tfz7Xa5+OrdtwDDttzk2TXoxcWG65f7p5eWYutzCLuw4pBO9B9q+vn8Hu9d1yhLlzR5cjfHXIW4juZ6V215fkmPryHnqW4Iq41+u6+n6JbO3jLqDdzFuWFdSVlvp/fQew0NSeqo8fHkPN2NI090w9nb+KjZft6EfvAwZ75pwg1J7llZIXjl2nne/ijWy1DyfXZkgJrbzntX6MQoETxyw0K//Z595TUBKnSn7jaClV9BVv6FqTcjg4Pv1iTpsfPGZKDb28EhXRTrZb6JFcWYHGq81MHRq62oq8v5QCoqUpf8+thk9HP5QTUQYbeTOnsMeVoxB1/rihIREdLV6e3sNaQ8sC6sD2pVJtIuisuqtkKsIqXdfJOgjZ9wsPTJq6vsygVw0+THaDhlo/9GXWNNu29oNmJ/yAllFJVHBo/k4MAPDYFbyaTnjW160S+xM30T0vy8VryXxHcdz3AffM+ajmS/s5AlJVF8V1Sd9Q7p3ec57vwdPhOao3/lq/YAkJI36v/ByPV3BkxKb735Xbbe/C6rbpnM6baRIeWSUFVOt41k1S2TveeERFHJ+qgTV0QojM3pya0x+Rx8pesF+ZKHFbqu1EZsvepDtpU5Gfj1RCIys70N9f50De3ESe7Yfg+HXYUcuHYWpDUPV+wF89y3hstIiSyj57uTQjdQ18jvfga7sFKsl2H/upwje7VqNLTEoCODOv57UVS3uwwsbfmj35cx8owkf2gXsma2Z3qjn+m2+OGQD2vTads5/HhHDq1sQtFwf3es6l9ksDe9DMsfW70zywen16PRS6tp9GYGR6+ycmxCZ+/qnVoDD5DpNjepNWswOOYMYAjBy/+8L0AbkJt381DuFV4heWc1f/vsgKGjYEVDTtzXgU8zf6BQL0Vo5TRMTWNx9yS6bHFyckQHEtbG4pBOzuulvmNcLhrfkUVhm7q0XqeDppF811b6PzaR7i8/xPWTJpJ811aExcK1mWexniom8bn1fgJRbZnM3n+3Y1NZLGXXpAXtSyWpMa4EB1ahkDV8Bsfva48SWU4TdM/4H/uuJRObdg0UWEJ4PwgAEcJpbLsAwVYV5MaLc6i3Ld3g/XvXwzWp/fiBKvu9Arz84CecHuYzIcw6vMpr+li8908siU1Ct9XjgbP9PGV9LvfbJawW4lbVYuHR9SEnhAGWHMpg+eSp5JbzpT42yLDda1Kn3Yzx9G1wOTc06MDYJt2ZmtyCD1OTeC6pg3cY7pQaeXopNdcd95Zx+HaNtaVVGzlEKTZqfxM4OohRIohRIgw3sMr8tBSIVSzec0Kiaxy83vDCmN7oV2MR0N0zLsjFMKx4Vjbs4h99h0HuSZLOrTXWMIeg9sgiRle7B3k4FyXiaJUrUBX+OW40Sz+eTs/M4SRMXl1RKQugb0IaJ8d1o/6yE7Db2NZgmcYRVyHRQmHxPVeCEsLvU9eQ1xzFnmvliKuQo//0rcOOmb8eS0I9XnthMb233knqqPWB57vRCgpo9NJqXMsbMzX5a+BFv2sAfjeq8eBt9N1+nok1D/Bhfi7ftKzjbWf543a9keTVclt9Mo6mzwQxG+ga+ztptJ0/nJ3dPidGiSD7RZ89Sv1zG7KXi6K5NQFou/QBUpdu8PWrlGjnzrG2nRXXRMET9ZcwNe8ylret5jfBIJ1lRC7exPYfdMr6daDpv3YztPp8bEKjVFpZMKY9ZRNrsbRNESiBfs+n0mtiOwRjj4xEdIcm51rB+u1+QudMp3hsh6DD7IcBEHUkSo3q3v1HH+tK50FbeaHuHD7f0I1Fv3Yk+Yn13j7L+qQ9N7bdysu1PiNG2BkQs9bJZU4AACAASURBVJ/NGU34aXsbbxmpvw9ndJtVgf0Yhjuze7A+pzF7Bxv/zp3UjQbvbEC6nBckNLPKmcBSZjs4eFkK8UpelV3kxi+6ixbf7oEPjX+PaNwdcXlr1PwiZP55tDPZwU/UNeZ/9zHHXC5i1FKcMb5h/uq3Z8Lbxiiqz/bbiOyXDTIPYfeZQjyCXZM6Nw+8F7lpF8vcj2mdj9bTe/e95KXYaTx7IzJMfywptvPiE3cTMz8DRI53+5fdP2Lk1jtJsO4PPXIRwjsSjV2Y+bfE6QiHsNrY+3p7tpatZVLTLt5AV29nr2HflC6kPLapSiv9RLgOMTExMTH5ezHj6ZqYmJhcQkyha2JiYnIJMYWuiYmJySXEFLomJiYmlxBT6JqYmJhcQkyha2JiYnIJMYWuiYmJySWk0mzAhbd2Yenb7zD1bDtG1thElLDSb8dttI/PYce5+pz8oREJMzehV0gtcrHZgMsHG/GUcee6EXJ6o5+xChUFBQWBKhQc0uldsKGWC9nWd+Aw2LDTL+NrH2Ww9KwhN473/94U62V+ZaR+OobEp4z4DuXLKBzcmZj/rAPwOYtrGqgq6NLrHJ3zTDcSPzuC6/DRgHpkTU/3D+8XhuyXutL06TXe/qiYabXgti6snjLTiIEweBuWenV5Yc0PXGZTSX95PHWm+yI0eepw/4bhMjvdiCkhLBaj7hWQDocR+jEliV0Ta5E6OsOvjL8lG7B6q1RTm7Ho1/nkacXcuONOovsdAEBJa8WBwXHsuccIQHRUK+b+cjFYQ2aedUe28jjvH3YV+s5TVI7Ob8Ha9Flc8dZEtr31sADoPPQtueatmeWWrFaeXcIurPS85z5+W/z4BWcl9kNRWeaaKwBaPTlFlrYtodnQzbx+cB2PJ3Ym6+OORB60ebMB23+tRsLCwwxZvoY5zRsZAdc1jWXaPN/zNasjDRqcZdVlC/izVOfrs52ZmuC/kOfNs81Y3rZawDvreV8d0slNDYIvxRV2O3vebceifu+QavXlSPO0w68vFBXtqnYs//ITnFLz9q0mjbgLngBVVqHSv89tLNn2kt/zlfNoR9aPeydkbIaK9Ln1blb8/pSf7CnUS7nytYkhswErf26h97bzTKq5n9RPx5D07Hp+LvtKADiOJUmHdHJLwy4Iu53SH+qzss13AdmA++y6kYH1MxkZl41VGCFJQ2UDDit01WrVuO7plcQoEUyqtROrMNZcL2j1OfFqNMV1y7C2UMl7pJQ77n4Qy4qN4Yq7aE50PU9MbuDSvKueeJDziYJbb/6Np+N9UZ6WfvcZ13e+AdeRwJVxLjSePdnJu6wWwFKvLgdHNGPnuOnebTvueo9e68YQ+YOvTUpUFDHf+JZt5kzsQJcBW2kbe4J9xXXZV1CbnF+aGMt5X1xN4no7ux9rZyz3dbP/yzRSh/gE7tLcTBzSyT6ni2xXDUp1G9XVIt5ISQOp0/TpNSzNzQwZ+er4Fcbz7TgRZTwEpaUUSRtWAW2HbefE9MBzXq6/nKFcYQjF2FhcLRsHxF4Qa7caK9P2ZZM6ej/fHsnwLtsEXzbghAdyOXplWcgVVHphIfM7JKF1snDw1fYkP+GLqoWU3qwfNdQoCpbWI8Zy2IgxvD2LxB0KV68ZyW8ffkhd1c5TBzJ5OamSqFhCQb+iLWAI3d5fTiLJaqT3FoogYnE1lHSF7ya8ARir3Ja9+Q4QgVWoPH+qFXqYeLtxagkTaxofhu9mTQMeDzhm77tdKk1O6eHEA764HA3eWOf9e9yeO4jhAKkjjUBKmtRpdOsuhCJwaRpzmjcCjOBHLV7yXwbefFSmsSIvFx5/bAwx32/2JVwVAjq3ZemCOUw6GhhRLU8r5vpHJ7qjnwVfXSUdDiNHGkbetMpW4B0e7fKmf/eEZh11tCs943Zze2yeV0jd+s2v/tfRNNIHbMOJRmG5JejBsAsrVqFi23fMu81zToHuCsgG7Mkc4cko5dCt5GnFbLtrKjc96fvYeISzh4opqDwoSNTy8ScuNhuwdv48Px1tzdPxu7EKlUyHg3F77uD0hrp06LmbgbU3cUu0Ecax/ov7yWrYlRqzQ0ez+luyAbu5/ooB1MjdRA1dsu6VaFLfG83uG6Z707EP+HkT37SsE/Tc/2zpQKp1m1crdZ08TaNXT/P8ra14Mn4bVqGio9PsiV3kfl9OmKj+IeZ2jJ9Or2EjOH6gFjhdqDYrTWLzkG4NeNGONrz94dfMSPFFCGtUOzDsZUWNQq1dG+TpKvVDz3Rjzb+IK+PMsA4U1xO0si4Cormz9hreqXWlkSEjCEJVKeiZyvJp7/FjUS3UcvnDvNmA3e21CpXcb1t593uyAefPjiG6X5lfPF2PBuPNBlxczOFrK88GXB7PMt6IH40PlF1YuSoCXqse55eSKBhxrx6hUC/l/by2NPvXZnSXEeRG6pLUu4114TlaDJ5MWJ619n0T0vyWlgZFt7KUNJbmZoYMel1VgQtGHFfeedhdtuaNC7Gszdzg2YArKOFP9f6eDzMG+W3z9F2Gw0lRPZWY8idJidi8hyseGsWf736AWqMG2rlz3ms8c7wnPf65mlembA14X5fmZtJr501YevuCjc8rjOO1+Z/Q1hYiXKaukXXVHMCGQzq5/3AvTlxRBJTxb70Jt+f63ocTzriAc99vuII7D9zE5n1NCDkAUWBs+q9Mqrkf13FfCNTLFjwEuKOMhckG7AK+nHcNn9TugZCQdHWIDMh/E+E13epxFJQYD6RDOnlgzx3E9D9ErPUo5z5NYPIVQ7j11RnUUaP5R+0NTN/b5L9W0XmFcQyKPusNBefKPuyX9aHFxJ3YbzRuvFWojIzL5RuCC13pCq7JFGq+teVOqfHH/mSSLeXCFgZZ221ZsRE9OhqtXTL5yVEUNBE02mLctD+veZdTugXwCV276oJy8W17bB/I8ecTiDwBUad1Yg8Uom3Y7j1+1uFVTDp+JQGqKKBERDC90a+Alf29/g293NWUkeRpxTw641Hq560LOK8idmHlw1RfgHFhtfHEkx1pjM80YRUqG9JnA88D7mzAqQ1Yddm/A4JaW4VKoV5K1qw2pI7YjnSWseeeGfR/+xqyx1x4GqAjrkLqq1GG9lC/DpwPHYAFXWN+s+U4pZXvX+pFbOlav31fJv5KsQ7jZo5lx2tBzpcyIG/YhaK0a4m+pWopyS1NGwdeH2i9aBypjtCxPTxMmX0zjRZsgdmB+34uaEtBok7dijs0jahjxjNaMfb13vQyI41CbvDrTWq6lH65Dq9A/qR1c87fcgfLJ0+tJEqx8Zydvk64s72o7lRGhpKlCoUVo7rxRIUQGFGKjZLx8bQ8mBUytoKIsPPpyL5MGu8/rGvxtO8e6CWlRjbg7MOkPuYLrONyf9SaTNnifSfLh3v9bxBe0z2XT9zcltDFSIPzXvOvGDv4Iap9t5kjA+pzzZAMrz0mp6wWllMF/FdCTigqHxy+mgEtvwEMdf/MfV2I/7cR2ESoKvueuQyHXOmNMNZ25SiSleCBpYVD9Qt5Z6lbmwP3J7G03nSc0hC43xc1oNnQzWGD61zX/ErU5Hhk7gnE6i1UXw3V3ZGyCpckUUfdxB2jRhFRLtauQ7NgL6dJRQ48SVNnhSe8XNDmTY46bG0fvBY5E9pjF2txSo1+w0Zi+WWjkUdu3SkerbmH9RPfpWvxg9SeEXz0IXVJxKky+u2+nmPf+dK567pC7ELplyED8At41Pj51XyW8ycQGObPKTVilAhS7t6CLNfP1/+xl4WtzsILDwetD2BomW5tU6gqasP6NLT4Rj/arr2ho2YJgVrdCIZjFSqxX6/1SzL56eFVOGUkz5zoQsIbq+G1MPX4C7hi7VWeodZjKqSjEQJhs3Hwho/oX+0qI/yopnkTnnpiHnva9fmoKdxVGtgOYbGw+kwSNZqfdWcUVkDqSE1D2O0sm/cpPe6/H7uyyd80VImp4Kk376Xfc77Y1iiCUPHuhcXCyfs6UaxnYBcWI67uuXyUiAj00lIWLZ8H4M0ykrymwijXfZ/Pp1aD5nEVi/dj1djJLCzyD3LvyeorNUHTzwTWX4xMMOUztHg4Oqod9d9e7ffu/bcIHwQyvS0FQ4ywhKpQaGLRWD1lJkwBMDSoQt1Jt813UHuSRNu3L3RZXHw2YKEIso/VQmshQbi1rudnkPGkk3Y24wuqyQxU9yTYdaMfIHnRRr/OU5ONSPp2YeXAoA/Af0TmToppZeyRq8gZ2xS5eTdU+ITopf43Sy8qRmQbkZE84SalZnzFqz1mo9+UAdiXbvILbp1zqgbJ+OxOp+5ox9Vj13FN3E5yymqx/ExL8p9u5E398n5KKtftOMfi1tWpyC13/AYYNipLoWEq0YuL+XR3Fx6/Yi8qCql37iFvRsCp7gZoRuba62wk6BXSLEndGx0qbbPxYlz21UMceMR3SJ1KsgEjDAER+VtdHNJpBCInTCByaVxXjauGs20S++60cPDGj7y7uz4ymmrK+tBZBFSVXa+mUKwvRRWCGn/WpKatmHG1/yBKaPT4bBJJL242Jn3DUNVswJrUgwrXSrMBl0Pfscd/QxWyAWunz3DsgXTqvbOax5O6UE+sg7cDy95/Mp49V86hzYSxKGWgXZHPc21/5JboPBxS4+hVFpIWXVhK+m+fepPnTvns0Er1OL/sK35Ncbl457EZ2IUFFxopy+8jhU04urfmlzmzvIHRez75EClfrveLRiYsFhy9L0eTG1k2ZVql9dJReOTbu2hm9c3BVMwGXPe30IHmh96zjMcf3Uvvofei/ralSn1xsYT/IGdso7TUN2iopkTgkE4OuwpZWBTFRkcZkcLGb2mfk/2iHdk1vL3uorMBCwWZ7z97qUmddLvVl2TOncYHIHp/XkDA4pw3I/0M4rvKitnvLPRe02ML/qjRn5x70QG6FhC315JQz6dlCYHaPIkT3yTTYFUEcmk99sxMM64rdeTOfdjuVznwSrpfnFit0Oo3TNrw4gy2d9CZmtKSb1vVpuDK0yi/bWbf21281/l5YHu6bPHvG0v9elwba5g+iqSOWuSb9HDt9+WIm5v4C5YG/ll2ff0qUGNj4bJymYDTmiNaNfMJHSHIvNwY5jWbVEn2ifLZgC0Wr9b0XcrSKiUlLe1ayOEn0tn1anP23aOCgE/P18EpNTIdDo73doVMxAjGS77vxpkAtPv4Ic71KGR/p1ImJnZjXNqN7LlnBndk7qtUm9sztX3IbMCeeLWa1PnTEfz1uaBswBXrIkSVsgFvecw9lBZKyI9Q2RnDNKg6wFooifqxGm++NoT2kx/gtn03sHfYDMZk7Q3bFxUZmDmCDX3LJQmda6HjuM3YRXD97aoI4920CystnzmF3j2NxJf24JSa953t8fCawI+cUMgeLCmUldtXrUIlRokg6bE1fqEVg2UDDoUidBzSyfIvPvk/1nSBuJhSHNKJXVhRhULyT6OwxjoY3/Y3zmoxdLCfxIrKtq5zaDFwHI2iOmBdsSloWX8pG7Az8EX7oqAWr+3si83iYnzKSu6udhKn1Bj+3XL+3dzfvqzrwu2uYgjAia37oNSuhYyK4HSnWuS1gs1Dp+CUGmvT5jPwt76UXH3C/4KeYR6AlPT8zyYm1Mjy2pm1Fjqfba/HVy0SkC4XrgPZ7B62gP7/9AWZtpyzBL2pit1uZBQWAulwUH230V5hsVLUIp7xNf0z9srqsSSoxUAMDgmUs0PZ8n19pUmdktYJWI8GGumEqnK+T0tWTH3Pe2882kfbKWNJeHO1f/bYlNCJJY1OrpAN2G2euHlfH75qtqjS+x+5KoZ6MzL8XsB58W1pv+EwbW02Dl73Mc2mjCb54dATVapQiBI2Gv9cbAzF3bm6tLw8viuKYXi108xNuQpt/6GQZRwc+CHhsgEX62XYhYUR/xnD/kmB+/9KNmAjlnE+vYaNwHJm40VnAwZQixXeP9eIelMCEzuWTIHEj+7n4PUf8cRz3WjyctVcGNd3+Ioz60sY5k7Xo197msxb2uOY/Lu/TVcILE0a4bHZArgO5aAcyuFIF7DmGu22CpXX62YGzAtITaNT84N/KUNv0GzAIYhTS7zPpyWp6UVfsyqED2Ke1oqWtY54tcBe2/9Bi/Hb0B0OfpQ1gBo8/+EAFl37LqnWCLYMeZfrfwmSmdTNX/FeUJz+gqTTi+Oo8/F6GgjDpDFXNubDW27mj7enc0vMaW7PzQt6PY9Q0YuKjHTpQqFm1gFqCoVbHu/CTTvPMDIum6+Tf8Sea/UrQ1YwL0yquZ9+TboGBK4+8GUaSUMyEXY77dcPpfWfPsN904UlvuGrotLlsdGcfUXQ8Jcyzje1kZ8CddqdoNbzxQiLBbVBPX774EM6Pf0oGz/2Xft8q5reTLxF0oIo8uVyq77f33Ul5xorST+H7nO7MNqpRBiakbDZcD4SeNyExT8Ar3v/fcxVSH1LYDZgj/dC34YdAJ2iq05hz7Uy81yD0JUAZJCnUTtzlklNu3ifi22Dp9Jjy0OBB5ZLxggg/sz0BYHXjfpN+nYYPYe8zSNLvueNZm3D1iUchneLxFUjuAni2CPdqP9W+Ay2HspnAzYqLlDjqrH0s1lc36Efrpxc7/zDBWUDBizFgh1FDUBxBv3QNx+7hesbDWTXqulctWVkleqb/sI4Nj4Xyl5VrhmqStksiUM6saCS7fKllBJWG2mvjmXV4297PUcCTDq6xvQmC2n93iQavhK+L4880Y0d4wN9IytmAw43vvmmZR2+EXXdtvOckMeVacEzxYRzM6xIWKGrZ+7kwOTOMO13rEJlRZv53FDmHhq5v67Nx22h5SFjMiBK2FAdGscndA1V5EVTPmGcKhRqz1wT0ImxX69FnaKgYkzmnHiwG2Fxz1R75nqExcJXz/Zn3NQPAJVivYy9s31aqiyXpFJYLLRdN4SGETnIQvfQXwgUux1XscX4OzKCLelf+SXNU1ZlsvedLiRPWAtSJ+7L9cS5X4ha7h9CoMbHc8u2I4yI20D/PrdRc1cGfOy7dvFd57xlFug2pMPhzWcWu7fAr5mNO4bO5FHe7VAvNfwahaYjgpj6ro3ymTgOP9uNu+5I4+f/fBpwnFW4++7Tdl7vBYCFV7Ug55kLTOVUYfgdpdiY/OwMYKJ/OxTB0detFOqlLCoOmK83TD4NSrGi0itS440wl6xMOThzX1c2vDCDgzd8BAQmhWwwI7MKyysMymcDNuppaLrt3hhLvWMVhE1VswG7UZyCurbz7JfBNXbpLMN1IJuTWhH9XvgNCKK2XyTS5WJZyx8AY2L7+tmTaGoxJr6lptHi9t1+I59gNvR4NZpGywoQUVGhPRcsFrTLC4LvC5YN2D1R6d3u9l4QVps3rVN57wU/pcvhIKbfAa9W7nmuPSnqv6U231Lbe/xFZQNWq8dxxdM+l6NpeSnkD+mEbhHoVshvBr8PeZP9TmhsiWTE4Z4c6xpBw9fWwVvhSr5ApI6s43ZxkTpn9JKAQ4TdTtbbaTjkeuzCio6Oq0d+wHHhEDYbp9IUrzkFQOT5bpBeWupdhSJdLhIG7WTfG11x1S4Dp0LEEStJH+4n9f6NFA7uzJ/vGPbFN6/owzXlRvfJE9aS83Q3EmcfxpVzBDCG7lmj6pDcPoclLX7i9TMp/GdIL+Zt3gHKPr8XLmdSOpkdpuExlaRYSzg7pwa6NNLvqIq/I/lzSQt5udzwzfMsSF1iO+di7NEunPy+qdfxW9MVbP5+6oDhOO+ZBrOUgCXrKN233kw0B/yOc0qNKMVG89G7vT6yqZ+OoXEbB3UzKk9ncjFIl4st6V/hkCqvTRlCHct6vxdZulws6z4NVdj5uTi8mSNckkGpS2p9vAZeCD2RJqIiobg4yJ4gx0ZUEIhCoERGsuWx6fSf3dPPhzawMqGzAQPoFqhrzQdZO+h+D3XUaD7bk87TbcIeBsDaZ9/D89z5XavcZ8bjtaDJTYa5R7GRNG0fmltwunqmMTdxFh5PpBUlUQHledgzMgJEq5D7AQ52/5jfg6ydqJgNWF27A7VhfXZNqO/z033nGK7swxx+vCOltY3FEknfBrcjC4uFQ0+ns3rEZGqoUTilRqHuYMDOoUT2DZ+1uDyVuoz9mpsCdTNxSo2Hauzjjle2kqvZSLO5XUBkFC4Mt7FjxXFhhwJV8V7wXwbs/kMoWA67JwWEQh01GtGhNXLTTuOBFIKT97TnwKAZ4LYsKSjU+nc0DPQvP9xKkZzxaey5d7q3jCjFZmikDwYe61l+2ezJ9UhdeoWi1qE1b6/9jlTrBop1jVuSr0IvC1zo0OjlNcjYWLJf7oqzoYPoHRGAzrHvm3DVuyOJXr4DWeJO8FbBTjp8yDKvHRmMl+aXy74s10bhbQMYkxlPDPbNOK8urevuVoH19y0c6KZQR/OfUFFsVqR7yCd+aUC+voYOvz5A9p3G/gZTMji+oBn1RxWjWSwhvBeMj6MSHU2TRSUcHBBJ8pNhVi1Kd7/qEuFJJikUcsd3xGM2KNbLePSFh9jwSYVz3SMvu7BSe4b/KEhYbYiWSSRajQmw10YPx2YJPUNd2vdyzt5fiFUN1K6qR5aypOW3gEqeXhLUE/xM/1Sqz6lCynv8swF77Lt6cTHNvh5Nct5aY5vA5+Lodv3yCOIb3nmMhlMz4BVfGZ6+q97pJPWs+UBtQ1nQNG85nonmI4+kA5k0uWNXqAVoXoKNAJYcysAp15Cva3icDqXLxaZnZ1B+9qrpogJq28p4vvYONLkJULyr1KYktwwo19m7A5DJtusq91wo1hXu/mM0qdbtfhNpnqy+BbqL/nsfo95GC86EGmy8+W3virTb5o1FZB9myK2/MK7GJqIUKzdNCJGJWFWp2eU4NdQoUueMwRWrQ6yTA70/odVTY2n8ekaVvF7CT6QJQf7meLgcviioz4DobIoltLTCxjKNYt1CI0sh/edMImn+OZRtWWGLu1iks4ymT63hH3OGsP/O2my/9z1+WviZnwB1yAycbmu5gqB9xjASfgg9ORDsA1Cor0WTNvfHROemzjeC8J+Aks4yZLd2lFWzYVuyHtE6hf1DqvPRPz6gR6SOJjdxTNPo+dBYouevA+EIrqlIiVZQQOK/NoIiUBolIHSJ60A2AO8dWsX46+5F273f3/WtZQqP15oXUFyotema1Hk3L9mIF/G1sW1GSjIlSxNRp8QTue900ImY4tRanLq3mB1dv8ApN/JVQSNjgsgtdKXLRZ1bDnL+xsvpsOAYuzsHd7NSIiPps+4Yy+5sQvKTO/2zAdeoQdZTzXHKjTikk7kTJjNx+b2oUqJHWDjXMpa+k/7g6XifVt923oMkz14LHqErBMJi5eC/OlCsrwucRRcCeXlzlnxnTES2/vgBmvyyzm/Cz2PnB8MrZOlHH4X2pMEzUlC5fdh4fvklcH9VBS6AbZnPhztregfatTxEydUn2H/bTPo9lk7pTwkcyq5NqxdyqTP/PKtWtiF5bj6fLPyQuxt3J2Fqht/QW2mVgv39PG6vl8HgmAxUoZCxWWfh3I40mXcU7cgxhNVCQf+2PPrqFwyM3kDvIfei6oEfoaW5mex3FjK2SXe/7cJuJ+vD1mzvNZNdZS4eHnQ/cstub2LK/KFdcEhj0tATK2VKwh8AOKSxDalz+/6bcFx9nGAcHCJwSGf4zLxuNKnT4vVCrybtwXduqVf+SwExit27vFcK45tmV5yGMP10DEmW0ItSBLC1rJTEf67x+bHngqtNkbFi9S8LXSlp9tZu+j6dxrnhXflqzhrD3/X0WeMCiop26hTNqu1AKyi4oCyoF4O2Zx9jbt5AhkPQxFJMTcVGlGKjWC9jlxPeyu3NjnktqffuahIUf/9HZ5mFE5qDCBH4Oc91Wch21eLz473ZsTyVpm9vQy8oMARukDaJNVuxW6wovyYwP2U2i4vjGbH6LqIzI6n/9hqQkhjLRkPbCtcn0hcgR9ufDZ3bcvV3JTwZv4deO4dg2Rn4ETvxesCmsBx2FbOiTyrg/3BHXncY2bkaE5b86Ger9aBJnWUlkSSvvJvmE4/hOnGSiqvipLOM6IUb2bXAhaN/JxKf3U3nagewurMBLz3VmrJH4lnaphiUPYGTObWqYylSaPPpA+4CQQ7FeLJ1UFww79ur+S7vahJWnEXfvptkZb1fn+b/1IwudbP5uu7b2IWdzDIXaZshQnFyS9xGLrNFsLJkC4mL7yN1xAaaWjICfDV77xxEg+h84ByuY8dJWn4vu3p9ENTbIk8rZui+fyBvKkAt8gkqbzZgZxm1V1fnVLdzyK7tABBrtqDWrs25a5oR+5/1FC1q4g3sU75PUmY7yLksiXhO0HPHAGzOQxR/WZ9m2Q5cR46yaW43kleeQ8/cSZ8No2jAjoAP3d4nI7i12jHe2tuHp7JqorgElsRC+t+6lscf+IM6ajRHXIVMOBTFa/+6kxlfrEVVtgTcm36NO3JiZDoZT00LqqAU638wZP+NlPbOQzr908+fuMZFyxWjSGlwkqtr76V/7Fba2qz8UWrhqb0DObu6Ho1fWA3KqYByPczu8TGWIGaMimhS56XTbfw8RS4YKVl+oiXLRl1J4p+Bc0W+i2nk/VaPyy6L8LoNFkoHySvH0GxIZqVZyj2Y2YBNTExMLiFmPF0TExOTS4gpdE1MTEwuIabQNTExMbmEmELXxMTE5BJiCl0TExOTS4gpdE1MTEwuIabQNTExMbmEVJoNGPAGich+qj13DvyVW+M2kmqN9maN+L0U7v5xFM2f2oleVOyX/fZCswGXR6m3NyC7aO3V1Znd5Bd0JMWyjChhrMTy1MUqVFpPG0vTr47gyj7sn73WYkG0SWXxImPJbKFeGrDiJV8v8YaTu/6KAbgOHfHLciqsNg4/3pEm351F32ksnRVWi18uqxMPduN8skbKg+u8q1YqZq89uqA1DW42nMqVqCj0khI/p39L/XrseaQpzR5d641mXzEb3nADtwAAIABJREFUcNEtnZk75S0ihGB4zzvR9oVf/10+u/KJru48Y4qKsIZ5DNwZjj3JMStmA9Y6t6LzexvZ0DkqaER+JSKCHuvPsPyB7qird/hnA1YGS4Tg4MtdyLp7hjdkYudnx1Frlm9VV/aLXdkzYoY3Y/NNuwexrOeUgGdDWCyojRrw3aoFRo62xM7+dVJUDj/dma2jpvH22RY80XqRAOjZ+zW54vNZYVehVcQurPQeci+//PJE0HpUZTmop07Bsuh6YnxkfdSJyGzrBWUD9tRBiY3l/cwfaGyJIttVzLV/jMeSHUH0EbhxzO88G2/EY84sc/FkYrrf81V+QcSusmKGTH6UujMDl7l+engVNVU7dmEN+r4Ki4Uj85qzsfOngJGBpuJS/JNaEWtLa/NJ7pUsvPI9XxmKinZ1O376PPhClYo4pUan18az9d2HvWUM2nmKf8Rmkf7tRFInZSLLylh69P+x997xVVT5///zzNySSggdEgIhjV4ChGrBFgiKbdG1ri4WwF6wry6uq6soigooLiiWVVCwU1RE6RASQi8hCTX0hJJ2y8z5/TG35pZcsHw/j98jr8fjPpI798yZMzPnvM/7lPfrZUQBVul19PplHFnjS9jxQhc2XTPFYw/c91JV3kHGKBa6vDue7XdP8wuFdj+j3Ha9ESYT5xeecQn4GtFu1ralZ68G7IbSqwuxbx1lferrmIXKMU16blJBMMQK2699m/IrbVz92mO0fivyMMizwYtl6+hiBlVYUIGh68bQ4p0YnLEqPZ7cyJR2KwEovHcK1vvM5HW9wOcmVGpHZPPruzM8YZ9DX3mYNtPXe6LChNnCzhk9KMudCcD3K7/2jzdXVE5fk037n6qQO0s9MfB6vx7YE8xYK2ywZhNt3i2gtc3GdwcLuKLDgACGpOpFnUga7o3iKXuiFxNGf8nA6DK22tvwzw9vov0Lq0h79DD7v+hO0hRzUDWCI/0Vkk1xrKzTEbbIDcbMlKVcTl/Pd61/F4QWvG/UYkxc8eYSjmrVnF6Y5jkuzBZ2Tu1F5zfPUDA4HmmrCZD3QQh0m41fBrbClFbLjhk9yLrTG8UlTCb2T8hh89+mYJPw2KHzKO5vo4W5wC+6J/X5Ai5b8Dd+mDfbj4jeF8JkovSF/my/ZSo2qXF5Ul+oH32oa6Q8v4qqO2188v6lPPmqcXjO7Ddxyw5dvPl6ztQFZ+UCSIiuY2n3eQB89tFbwJMBaXa93peM+xrWpgM4fP+AoMdrvksiOreMzLtd3LxnoQaMorL3qRy23P02X1W3Y2zHNJCSDNNmj+jmmnfMqOUGudNTqV6+jMXlRVRqNZQ4dL/w31YEp0a8Pe0iTl+bzU+vvklcvd+EycSJW/uzYcDbmIUZTeroSHSX3XAb3+ZKNKNia5h6wZEA8ckWL+xBk5IqGV4N2I2kz0vAoFyg4u+DuCb+VRxSIjSv/fNVCZau40IThmowxm9uHglPeH0EzI1mxdsh+XKj1EeDRtfUtg0VL9n4qtNCrCKKUcXD2VrQkYxPzqCcruVUn1aUX6Qz/ZLZDIuGq8f8wrp5HYPmFQmfri9+9KFc3PdEDl3M6zwP4dIbbidpzTbQNKxA6Tcw/IK7WPLRTA//7557u3kz0zV+fXcGYJDe5LbrTRvzeoML1wXpdJA5poiLfxjFgi7zcEiN/c8M9svjxRdn8FJaT6QQqM0SWbD5Z6CIZ472YPXxVLISovjl62xSvj8FFPBmya/c12GI9xqDetHk5gNoLgP1r7J8cqxF5PW8mLkn2oKUtGcV5PTgoplreLDZ+2QXPEDyysCZoKsuM8i8d9jagSNyo+uBEKiJCSye+wHL6qBCq99sDNTpZoauHEfqXzd5GsXJ67Pp8voJjrwiaHGFYXCVuDgW7lzuGXXktusNiopeXc3xf+t0ebiSY7d6yUSk08nW+6ahSYOftri/4ZX68jO4vyv5W+my8hbyB71HfbiNd9HNb6AKC1cnuYxIsGhLReWFo0MNPaxXDW2xRNfIZlRSf+JM+wKMh19ZdEmenu1SA/YZJfl0OJEaXIA2b6yCyQ958hCqitQ05nf9mJutFzWoBvzIxQv4aO1Iv2dRPKkfG0dPJut/D5H2mJfwvb6XOjylX8CxGt3OyAkP02SeV4J9cXkRN++5EKdUqHJY+S5zoVc5WeogwSad/s/NNTrOf2E6bjaxXQ47W+1taKrUMCDqNAnCeO6qUBh+xU2g7PC/OV3js9SfWVOncsPP4yN6npmH13v+P5UJA+c/gtCh7UrpEZb1VQluucFwDNqulAzlUQ+F7J57I7rcOSG80VVUSu/qxPZe0zjgtHHx7PvpNKeS9K1rkUJB0zXiikvJ/FzwZrMh3PtUJpkvFaOdOBQ227OFdDrZNn4aYMEhNb6sbobya6BopHnFFnr/ZzxrH5+CJiV1bbxeUcXfB6HJQpxo9H7vATqY1wc0bje/runS/VyZfRv2xCja/7TKI6R46qaBvHxlZ1CKQddIWmAYiRFZ56FXVWESBykB2uurODJ+MF9WN+OlN2+ilY+q7p77IfWGE57vOVYznebdTUbFen8jsX4bP/eI5Wf6k6ysDYiNV7p3ZlKbzwAoqWuFtP02ysRn77uTqMUbQg6LO5m2+T3vo4MkcQfiKeg7i1x6I0xmdk7NoEZfQoxiQZM6z5YWsqI6ixrdwsSWc7kw/U5O9PVajWPjBuGQBSgIhj55L02VdSGlUqTTScrozcSVR7H7kA9VoRAoMTFsvW8aNinoOnU8KcHerRu6xujEArb4ePq+w91IpwW22mtZXNWNR9u68mjRAu1YaC6BSKB2zeRU16bEzVvPTe2HGJ56A2H633RrQYLIhw+N70rTBEquf4cby0aQ9vi6sOeH4qgNhoqrLciaWvQzFV6lYClZtG89Lx4/w8MHc5nty1EvJQt3G/XeJh1MPNqXgj7Bl5CU2FiDUS/Eu3/8/nF0XhrIMxH0nnz+T3/RO5rUbTbP+ZkTfEaNmoZ0Oon7qoCMb3280/9nRlfXmH3bFDSpUqGbSH//EM49+z3GyQMp0SoqSXtkzR+iBnzm+oEebk6zUHln/F8wK0UBL0nabLR+ey0XnLiPuIN2Mn5dB64OsuUte9GRWIWZlImrCcs5ISVyww4sivB7ia/86x3+3cnrrU9JWsqHp9sZ5Djg90xaz1jHzGmptDat88vDZNYC5vxa5hvKt35ELKH+d2HHw16/Ynd1S6Q9suHXuaJ+hc+4Zy1zD6wGvHIqwocRXRUKA606OVa396Ly43/fMYb944wjnW/e4UmbOKfQTzk4FHLb9SadDXCju2CSf25cgiZVrMJM+3+HVwgAeK5T3wZSNIyHOxpE/Y+6R2P1eXHPAcVPR3NV53Vs+twrCdUg6rXFJl8b55wcJQhwi4OdGymcTo9yylXFuTh/Mu43Z0MGLR7UDPkjt6yeycTul/tRoxsjU6swhzS4YFBZBtOKc9NPRn23LmJSeF/4qgG3XGIl8X8uEvUgaw/S6YyIIez3QFija0pqR7G9DTnWE9y9/SaalJaETvwHEudMeslfisP8UxhOVl2j6Zz1riJ5y3RTu7UoromZN8pW8mDHBlQl9MCh3PlR8G+f7zGKhcLqjgizEjgkdr3A+sbKuSve4N/9DVCbJvD+BV5C2YNVCcTbzo6w/fdAQ/pVqlD8eKLqz3ONbbvU806CNYRIoMTHk2M1Y5MOMr4cR6Z5Q2gv9w+Em4z+tyDzkcNs6tADxOZzbk/vdvgeiEY7UfGby1Mf7qm4uksqPWzNzQCtnlyVdDrZfcM7uEem2VPuI8kUhms2GJOfyUz15X2A9YHpI4SvGvDQikdJnBMZ9eIfjfBbxhSFwVGGgN+JjcHomv8c3D73HvSIidOMl17/Ba88neHJo4slNFN9OBxwVp3Teb4QQW5DRi6vBEDtwEzSzac934+djIt8tfz/EHqYa8KSykeCHa9neZQ+Mu5Z+6cZXGEy+YlKnrx1UKDI5FlC/k9h3Efzf5MDM+Okj/xDCMHK3wrpsPt96pe38rZBaC6PxSxU2r2y6qymMtwov8bOod/Q5nzVgBsSpvwzEb4oqsIXZwxOUMtJ4REu/LORstgWdjUwEmypaBt48Cwr5Rn9t785y0kRMF0QzBCHQ1VbE818SMsdVcEJzP+vI9+W4Gmc54oLu+80SLH/ZEin0+89Nv1w9W+W7taHn+C984b8JmM5f78x/WVKTvLsVPizcTxbx1l/ovEsOxKpaQxKK2NBdfo5l0MVCqpQjBGZ6f8OhW3YtyIrTnLKGUONbmf0Db94hAtDZhYb+7sWzg11aXBJ91DwaFz5eh7TW/ob7pweZ10pF1eH12qKBCmf7UNt3cpoWEIwcMJYzn9wTYMemq9u14kL7H5KEZbD5y5t/1uw1e6vVSfrueya1HFIDYfU0KROicPfa5m87zLP6EOYTBEbG9/Of3LSYoAGdc+Mi/x+nt/i8iI+2LfC812/oE+Y1JHB8kMi41f8+ps83diXEwDY/niyV/LoT8ak4Z8GEJC7RVMjhq7xTsoiXvh11Nmf60JuUh/PJ+Oetec8hfV7I6zV0aqq+fG1ocQoFp5ruY3yL11Gx2UwPJVYCEzJSex4tVvozH4jDjmr0KSOTToonzA49ItQVA6Py2HE1pOonVI8h6O/8kr32KSDo0+f/RarL57NxZTawXPvqV/fxRlHFEJVDAMf6uMD54GDnDo/1aXtppDwaT6bsiVycC/koHqfwb3Qz+vDgacGc8Jnq9VbQz/xyzP6iDB0wFyfszUuDqnVF4XwQ7BnXfz2ACZccqPfMelUPAKFmtRZY4PJFZ154XhPVKFw3/DbKX7buy+1aloyZqGiSZ1jf+/vWTgJh3kH1qD17ez5Pr+qEzbp4O5f/xZWUNJdRytuHxQ+3VmgleqdplKWb/K7VsTwSatdL5ly6/XeOhNJPkL41TH1F8NBKb323XOactJDLFnN3PAN9xdvx3lR3wanUQZHlftNG31zMJ/T12QHJqxX9vqIU6Lo+JU85ykjYbF4Pz7vXFit3o/ruDCZ/I67kduuN7ntepPyz1XeLa+uMrt/E2YL0unkp+7xXJ7U13M8FBrcvdD0o9VMfLQrz7XcxtJ+7zF+xSh2fpFFuxlFKAlN0CoqifmxCe93+oI4YaVguMbEQZef00MKBWG2cOnUxyi4fwpWYWbzQ9MY/kZOQOORTic91+tMbPUGZqHy/uV53jxMJgY+NpblL0/FKsxs6P8ZeWnXoJXsRSjCWNzyFf8LMlSMnbeWG3fu92xMzxy3juLFqcgRrVCcMqjhslbYEGu2+BRSEjdvPabUDtRktcKyyNBjEqvqaVQJgdKrC4cn6mzt/z7Dbr8DgFM3D2RkjH+gxOBbC9lyuXe/zrA2u5jY0l9CZUTWeZ7//RqWw8kTh/vjvP8ER8emUR+aruDIT6T9v/0FR1utEWAx0/m98XRgFdJhJ/Pv64krj/IEnzzfyb2XU9Lln+NJdR6meaG3McZ+sRbeNIaBBf+cTu6M0BXVHYwRpxT5eYLvvHI1f/nXZNQKc/iRi5R8v/Y7ctv1DnhNvhppEc3LBptG8NWxa94M7fiJwDRBoMR5d6E4jxxFHD2OEmVl4e5V5HVrQA0YeGfPckZN9VEDFoKRQ67k+5Vfk7XezM5+DTgXLo0595xrtLCwavI7MNm7rz43uS9yQHd+mDeb4R+9FzAPX6Pb2etj4PNeeYw1T07xRJFZhZlfJ0/F/LrxbB1SY1mdhf8dG8Ta8g6eyMxgWDorcF92MBTY7H5BHgClE7NxRkvPPt24rwqQmuanEtxigyBxTiFVV/Tm0BDh2acbEkJATjfGfTSfq2KrOKXXkv3FQ4aAbYSIqMtf1SeaNbs10s3wUeoiyh/6mtTH4jyb4E/ptcQIC4tqY3hq6t9Jtu4Pmk8kasDBIB12kl5ehfUBs+eai/auY+ima9Bmt8JyRke163w3axqqEDikTuef7yZj6nqY7MrD6aTpZ+vp2v1edtwyFYBvf/mCfxztza8vDqLJzlOIg0cRFgtaUgso2hHoKQjBJwO6s2tWBpljCkBKokfsAz10+O0ju7cyae9w/4O6hnPPPixle6m9KofD19uQUvCP7O+4tclxeqy9kWs6beT7NxNo83QluVt6YxHGjo0RE5b5Gwng9XbL0dr6btfyVwPWpM6Ol72Kq4trjCGoUFW0qmq2DjQRq+0l5OSQrvHBvhXcluKNUGo6p5Dt03qRNe0U+GyBC+jhpUSYTHT8ooLtTyWSdWc+/Nf7PPMuHs2CJZ/jkBqT96z2bMXyeNdSN1bDX8pmc94bQBSm495pimbvr0Z/XvLoyG/48rEwi71LkplccTroT5V6LS3UWL47WMBl267hdJiItGYxtSzq/DWghFQDPn55JokfRKgGPMTrtbu3f+k1NaR+dReZlesaVAO+cspjJL25Dv7jzUPbf5C+E8ex5tm3mbkthS+7GvuajSkc43y3kVVbtaTk3jQ6fV4JQF6S4ZH6tVVdQ6zeGNR7E2YLp6/JZuFrr3uOtZq6muwmD7D13mlo0pjf9Q3jNQuV86PsXJyyguEXOJD1IxnBmP4jMntRo9u5buXdZJi3+nnFCbvg+4mvoUkj8CHjWxU0zU8leEiz+2n+lZVDQwQrrn2VeMVtEh8NfjEpWTz/Q4befzfT5xn7oDP6V5/FMn+ERhep81ynvpR92ovslP38I+l742alnQQRzZdVHZi09VI6PGOnzY7VOP+g7WO57XrzctlaeliMEMLvu39C1KsmFIxhrSaN2O73T3ck/ZbA4AnpdJL2TD6jZlzD7F8/IVGJ5rlWBWivr/fwNoDREy+pjQmUhpYS7dRpMv++nt4boKgPAfLoanw8WteOlF4dS/EtxpaVl19sBcPqF8YoXfQ3BXRaaELabDQpriP3qltIKtzOGqeZ9eXTvRXdlX7l+Bxsc4o8nBNgeBLhwhQX1cbQ5YkdMNb4PjUjk1ML0jHNbE7ChiNINYSHKAS29k2ZMWsKUUKh8vsMb/EddrLGb0Qb3I3LiipZ0rdF0DkzYbWSvaaW9fdGkXXXZv+OTEr04jLy+lzGt4WL6GaJZnG5Ed335Zx+WE9KTmVI1o5+jUSlkM12wZM9z0OvKvW7xl/bD+aj/SuZfcMVNPks3/tOXI35rl2lXBVbSO7NdwQVYbz+b/ez5OOZ6Ogs6vpF6AfpghNQUUKqATf7OD/iRmj5Ifh6RdYDhtBh7cIU9u9tEbEaMBj1vOXMfK78eTQLfp3P2PKDLKlVuWv1rXDUih6tM2bIMp5p4Y0AG7Lp7oAyLC4vYlkdfnvTAeO5/pTEN52/xCrM1OgrOKDpJHoKIGn/yjpyX+xN3eU5nPfCal5oZfA82KQDBWO/fV7n85GO6qCefHVKTIBzEQpWYSLpc3PAvuRms1Yz/9FM/hK/y8+D9VUJFq7jUpXEK6aI1IcBjmYrpM4DFBVb8yjOZtY5QqNrFCz1ho1UCsHDchCiTzdqOsQac6WKSrLchvZHi1wKweOpxpygEh9P8XPdWDD6NTqYLPxaG8O9628g46GjOI8cgxBhGtLpxLlnPze1H4Iwmai8sT9Hh9mJjrehKJLqk9G0/cFE/GchhgtSghAU9ZHs++dgtt81LSDJdvsiXjo0nC7vjCfl+VWYRJiFQF1D2oyyTs9IR+nl5PM9K3BInfRPHiENf49JrN7MqFvG8t4Hb5JqDhewamBlnc7U4SPQzuzxO55weRmmDg7yvi/knqbBRybgHnrHkTVrHB2fWe0XGy8ddpTlm/ipezz6BV1p/uJeLkzc6VEDXnoii9NPJFPQpwihbA5Q4QXjfWhHj5GX3BdTSjK77knmb8OXMvPOt+hkruPjUz0YvHIcGQ8ecSkSVwVtpLekDCV+YC0pq6M45YiiS/xhzo/bwdIzXXn8mxuZMbcadV3w92D6pYi8zufD13F8mjEv7P7jU3otN+2+Bu2KKj814Pr3hKIiBxrbt8SqjaitW3FyWCfi5+ZjX5yM5VJjK2bICDyHHRSVuk/bnJUasN9zLdlLbrveqFnplP4rmtcGfE5XyxHMSH6sySR96e2kvK9g/qmAOMV/P+zwlH4cHpdD/hPB1YBtch3982+l3egSj3f5o2/dcJUrelEh+d+byKUPakITtr+awfvDZvFSWk8QoRXE4xduQX0zsoVuVShEf1sQtH592bUlM/8+ClNmRFk1DEUl75LreHzel4y57TCn9FoG/ncAKYsiz6JRDbgRjWhEI/5E/B/aMtyIRjSiEf//R6PRbUQjGtGIPxGNRrcRjWhEI/5ENBrdRjSiEY34E9FodBvRiEY04k9Eo9FtRCMa0Yg/EY1GtxGNaEQj/kQ0qAZ8+MHBfP7QJDbb2nJ57AkePzwIFZ3+cWW8vCOXM8VNyXhucwD7+7mqAfuGGrrz6LvgabmmjyFNoyM9kWO+6q3uMMOu08bT/kWvvI07jxe2XC4nNN/mJ4jnhiZ1aqXdE42S+s1dZI71EuS483hv53lybpc2IcsuTCaDw0HXEGYLu17rYygC++Tx6rZcubh7k5B5eDMzoqmcF/XFUlGLvnE7P2pz/dSAAdSsdP6z+GM6mCQ3DrkO5979FE8dQOnV7zJo47U0ySv1vBd3Ge5cf6vck1PrKTNBiGakzWZE2GV0YvvDzT3Po74acNWoPnR8ZCfHhpwKyv6vxMRwZf4e5o+5BGX9dn81YNNfpRzUgx8+/wDwvntTchK7x6VwyxVLPVFTdx8YhLvM9ctR/5putdfcpD6GIodro77auhULNvzAhMN92NRP8ajwDr16klw2fUZYNWAFBQXhCWlN/f5OMu9aH6DCGwnqK4e476XrU6/Luh61pN20gZfL1vJ46gB2/bcf0WUWjxpw1JJ4Wr67zrgvn5BXT1sZM1mueWGq57gqFHK3X07xlmTiOpwis/kxvkj7CYDs9dfTctROvzzc7dUmHYxK8pIs+ZXfamXnlF4sGP4GmeYoRl55K7Jwe1BVY4RAsVrputLOa20LPWK2qlAocVRx32W3oRWXBbRXd/0qe64vG26b4seqFw4j8m5kceFEP9tTpdfRc/4DDaoBr7t6MotrUvgwq72nHI5DaRKg+3v3sv2uaR5tOKVbFt8s/gSzUKnR7eRtu46oKw4hbTbmHlhNnLBiblsSNE40rKerNk1g2v1vk2qK4vLYE1iFmUdb/sJrbQu5Nu44G/p/xu4b32Hill/Y/9SgiB7KucDyYTMPN6YvPeMXVW2o0R0eIUqAdWMnU/x+rwAyHFXonpftZgFyf0aOuoVBUx6mUqvBJh1svfxtvjsYqE5RUNXR+MdljK7dfpRvDuazuLyImftWcNWmQ5wYY5BuSIedjPvXBkTz7K710jqqLVuyuLyIx0o2U/K/3ux6rz/7Pu9B7LKWlH3aAwDzso3sviEBe26/oM/mxICW9LREUeZQwO4AIWi31Pjt7tTlKNZAHoF/t/3Jcx9KfDx6387o2f4ft9HXdu8hc+w6vjywzj8TReXk1ykkrNzD8QuCyK24oNfU8HX/jiBg16vZ/oxluoZYGRjt5Cw/TOpz+SzvHculN9wOwJtJyxrm7nAxiWlSZ/iOkSAUP+Omn6jgzv1DeKH1OpTu3pDmBVPfBIyO+42Krrx4vLff5z/He/HpmdaoQsHqUrXdkTeN43cODFqM4inBj7tR+nxwY5b0ylrSbjHYyu7ZeQMAmXdtoP1La9HOnKH9ddtp9Z4R5rz/s4ygeTR7fzV5SdnkJWXzS52L7+DiA2Q+Wki7vxRz5oJKcpP6MHLoVeT3/TSgnldqNQx+aCxXdhwSJHcD0mYjc+w6Huw42HBgGpC+0uvq2DrQxIj0wXxTnYgqFBxSo6Mphq9/nsOIzcHJgaTTwai8NWfHpV281/NvlV5HlV7HGd0ZoAbsPu6rBuyQktyYfZy4w2vL3HbHD0IJsJy+UlXmIDLzvgjr6Uq7g8POppijTvPZmRY8ve4qorZFU5PqoEfWfro0OcyLrQrJsZr56q5JXGmaQMrEVSHzi0QNOFjDipu7hqKXbfSwmFGFgiZ1Lrx3HLHfFvChlgJSUvZZTzafN5M4JYrSS2cxsv2VOPfsa/B6AHLDDpI2Cv46aTDFUwew86ppOKRGz0LBpmzvwzxcGw+KA3SNBb/OB2DEZbegb/HGsLfKOELJnJ50vH4TCMGLx7M4+rWX1OSkPRqEA8ViZvvEVIZuuobYvL2kq9s8aaqdDlLlMYrfHEDWE5tp/6Odt/77NuDDJgUgBDG3GCqBJY6WSIchm2KtdFKl13Fbk6PM7XA+7Nwd9L6FqnJmWCY/vfU231U3RxXeOM6rYquM9+XyQMxC9VJ7AgceH0CHm7ZT93k8pkvsCLOF4lnd2DzsXQ+PxfDUAUi7Hb26mrIrY0ifU0NtbgSK0D5SSeqabfR6eTyFj72NJnXe2LMqpNSSMJnZPiGJWmmndnIS0cqRAMml/E96UffYT5jfOuk55h7h1Kfu88UqPZlPRHuUmBhe2vIzPSxm5j49CXg4IG3GA+EZp1Kf8gnt9qVv1DVPh/xj98+4xnpeoBowKmrXTD7tO5MJDDS85mCqDIrKHctuoyx3pnFqPXpE55795CUZqsZjdpUxMzMVgH8cHsaFT6zixdc3BbTXxeVFXLxtFKZLvO1qblUC//liFj0swfmM3V6947weVGZamJFZwwxgYmkBfazGqGFc02LuO6h6yHY8kJIXWufT/YP7Sfv4ODhDkMSbTRTf2pziW6ejV1d7Dvuq/kasBqyA6dx50yNCWE9XJLVhzlGjV86wHKHzY4doP7mAzLvysV14hKI+kP2KIZuZZopm011vYWobevh9zhCCKUcu8bDRq0Ih5su1hhfj5oX46ybO6HY0qaNJnX//PDdyMmhd83hEGfes5a3KDMxCZVKbDX7JapwWv1j5rtPHQ+k+P15hbXcZHa/fhJpvR24mAAAgAElEQVSYCFLSL6aUH7Nnes4pO93MuJ6UYJIcrYxHsZiDyp8k7DLkuM97bQ0Pl4wOKLbaLJGl3b4GoNTe0vB0AbXGyUnduJ/tTySEvXUpDA9vRmYnpmekMz0jnXe6dqXLu/6S12ahsj5ntue7kFDXtxNLun4TkKd7yLXrvW4Ik9EYi2+ZjrnsCBVdz47LVtpstJmyyuNxdLHEoPTsHDytw85Xo6YQp0QR9V1wTa7YQxpRwsSrHeeFvqiuBX7A8Npqang8dQCqUEgLwX2h9OoS9HgwmDq0r3cTEqSk24J7jCme+nVY19C2FzOho+FN75+QE3Q0IxRBVFkYsUzdEEhNXTyG6+JOYUpqB0Bxjj2siOTDHX/wc4xmdcti/JMPYJOBz1qYTJQ/kMPi8iK+/XA6hc9O57rthzl9w0Ce69SX2ac74JCG4bVJJ2UvBY6WrcJM2vuH0YvL0HYH/8jSfYggNMCdn9lO52e2k/XcNuK+LfLYi8wJRWROKCLjiQ2GWKXNRty3RWQ9t43O/9jmpyL8RyA8iXlxKfmb0tCkTrrZqHjS7uoxXZWhzZRVpH5zl8edru2WFDSv3wQpGZRQElaWRZhMDP/no+hIVKHQO0hFjAhC8MPfBnmGNJW3eStC+Wn/uVjV5pKx9ngiEqGq1Fw9AK3SoMq7LMbB4JXjPOccPeHNQ6lWMVucSC2wxqhL29Hq7VWoLZozseVW1OtqAtIcuTbL8//e2haed6OesVGhG8Zt66XT/UiZfwt86fmS/rOKbz6YGia1/5ALYPTSQtq9EnokFDIfk4n0pbdTo9uxSQc7HgxN9NPTEp4lqqqd8V6T1XNU24igI3fGR/689bh6en1CIKxWyi5/D7VJk+BE5j5lmHv3a5SPDUIQDpgCq4x/Nrqk8xRjnnzHhJSAvIPh2Um3h8/UDSE49EUGax56A4DuP48lt11v5nZtS5M5Bof0vC6tPO0sRrHw3Y2v+p3vnorSdpd5HawgH91m45JLN/DBaX+izZ3T0tg5LY0db6VTeX22Z8pR2mzej9NpEF9dn82Ot9I95/yRCD+n26I5Vw4wmJlihAXnocNBF0s6fCvZ5ajGLFSaPhvZkP5sMSS6BAWBJnX2BRGrk04nzWeu9qNnPPh4A4q/wSAlssDo6Wp0O8f7eg1i1Sl/9indBNRT9pVOJ6ZaHWG2sGdOTwDSx3rnmZoucxkFXdJ6DQxtX+oZ+rkrReyylmjDjGmDb9Z9R+ayW9EqA9V+qy72DqUO1DRFd3m6SnUtFZrRmGMUC3UX9zy7ZxAhIqXBc+O2JkfP6TpSlyR/bCJGsWBC5fGBCwMTCeEZZQ3fMTI4GblQqM6pxSE1vqwOopn3O0FZETlvtO/UFGDUP5uNXpPGo50+Hd4ICsHdjz5ImzeCd2Tm6gY6CKmjFxnTWindDkVc5oYgTCYOPDmI1f1mE6NYGJkzkswxLiJ/KYMyq2lS57Ujl3jzUFVqRkYwFeXK89W2y5i4/Eq/NYONF05n44XTWTnsTY73kUEXjAFQVY73kawc9qbnnD8S4acX4mLJSzDo6wpshJw6MFc5yTTHokmdNtFnqL0yJ2i634IExXhRTjTeOn5egwz/OjqO7LNXEhUmE2euN4ZuqhAQ7x02NSnwGpkim42a9k7vfJqrPIcfGIxl8XqkprHzvA956khPtJNeg9lixmrkkN5Ip4P4OWt4N9k7v3d0fhq73u9L9fnHADhvUx2HtBpS/7opUMzSZOKqTK9ETFllM08aWVXDSd3rQR0e8P9GQ+13g65hXWh4R6pQuLlJSUASYTKz89GO1Oh26l5rF1QfTDrsvD3gfygoPLPqqj+suMUfBvc8g2H/0/UcAyFQmyawccI0o72Fk+yRkqVTplH6nyCL2EIhqqIho+v9/UT1uSlkB81W09h6ryEoMOFwH5wHDgZK7giBqU1rz1dVKJQ+nOXzu8KBa5wsqY1sES1GsdDxCx/1F85ODVgqEKOonnP+SIQtinPPPhyo6Eie2H0tMjH4VidnlPFgdCS/7ksj+tvAlf/fE4fqQs9Tzq1KcG1LUXAcCc6L6qvTVR/S6eTFF2d4VWqrvHOQibscHuP6QcUQYltXexc/dI2q0QNo924hwmSm/JEB1Oh2fvnP4IAOYm+et1wTDhuChsVTB2D+ohmZtxcgrFbU1q14psUORk5+LLiml6oyKM67QFZ1xsfrtNmo072G1pb858iS/5HwDDWlzm5HEIOqafTsX0KMYiFmTUlIye9h0VWYhUqz1X9cw8q4NXIh1foySEiJdvIUF98yJvjIsh5G5d1MpyeCqFRIHZMtcqXlmurfT+n7xJiB2KQDTUoKK9oH1dgTFgvb/tnBs+sI/EcIUtMYnF7K3BM5EWvOxWw77Pfez0oN2CRJUKKD71Y4B4RTuQ6bu9KzM8vOdMYsVH7p/hXiVFXgA5CSk+MNj9IsVGpORf9mKepgKLS18ijHbj/RKqTS6cCogygIzEIl88PqoGlKr3k3+EUUFXJ6cGG07tke5KvpZVmUT12eIcy3444stgw0BCLVjE44L+pL3Odr0e0OlKYJbH5oGqtt0cTPWROgLuGI98qtuEUpM+5ZS+KnBQizBb1fFxZs+IGMj8fR9u3gC0L0zOSqWK8nrx6M8uSvnT7NCc0773ln/+XB7/c3Yp0tvP5WfXXgicfOUU1ZUdFzjHN1JG8duTgwja4xP/1HALQTFSGNlVWYMQuVFjMik9M5Fxx6JPJpLfeoygOXp7vko5kNe7rAwoWfUvpK8O2appoGjK5r2yJAdFFo4vazxbtPT/E85zvbLw/wcoXZgnNQN8pGzfBMB44YcYO/c6FrTE1ZyLLv+3gWY0PeQ7qx88K5/4Dfe49UDVjabGTcs9Yv/R+JsEZX37SDz5cbXmGlVoPWrrmxR82tBKyoOC/qy+LsmTikxv3l/bEc+v2HssJs4YFlN7qkvCUVx+NDpk0xxXl6Krl+S8h0Qa+jCM6kxvr1vs1m+TfOxAl7jd0HRdvI/HAcB54cjFZcimlpocfjXbDRaPyvpPUIvIiUZNy/luK3Bng8YGXdVqPyNE+k+P1u/PD5Bwx4fBxpT+UHN7iKSslD/t5zTLlrv6HJTOvVTfzUIO5vttFYlAl2z+fIYb9rVj8mXnhNyN91dKTTv3qt6mVh13vB96iGg1AEJWMFNbodBcHWt7qHTPtLbfAqLcwW9j07mFN6LZMq0s5Osdcvo4bPS5re8H5iN5rM81dscHu6vV4ZH9rT9VELHvToWDo9FrwD0azhPTahqhy6ztjvm/LFwfBlPgt8VDHYo2X41/hK5KBehrfrKvfRO/ry0/9medpZ3sWjkVuLA+p6ghJN6rTi8GrAUrLjvtZBfzprNWCf9G4EUwMWikDfUuxR/r06OQfLpXs9Rv3q5JzA7W8+CLt/R23ShM+veAtNmlCE4FRGLI6eOah2qOwCpswzbBs8E00abvl323qQ8fTv70FIh53MMeuJKbdgkw6GZJVwXDVE5hCKoebrdJKworlHg+m8x+6hqWl9w5nXw6DHjCAAm3Qw8Whf/x8VlVMvpWDhCEhJ6lPrPCKB7v2I7u00IzoNBBFkyw+AEKTNsWFKaovz4CHU1q1oM+80M1MWs7JOJzepL4lqCIMLHL5/ANsueAvwGt6eN2yhx11n+Ev8RpJN0YDq0Zi69po74fRmT1q3/yN1ieWkk/EHB3L0646e3QaarmANov1VpdfhNt3J36lozZqQ8dE4OhH4zuOUKDL/vh4pBGqTJgycMBZuhi6TjsGYoLcVEtLppOTi98GlRJXwyRr4yPu7MJmoGZlNlb6Gu+Y8RCdzQUBDlQ4728dOA6L5qXs8hFMxC7Ze4JpGUqKjeWHrL2jSxB5nDcFCFERMNNSE3jqgNm3q2d0SMA3iusbGx6aRNzuIGrCiog/pyY9z3ie3XW8S5q4PeScn08M0b8WI1NvwtCE35SzbGzqtD9Y8+za+9c4NX4XpnedbMRd70/wwbzZdZown+bz9/NjlW2wyH4dUWG1TDf01pTTk6NjtwIRHUdC5X1/V34jVgBUw1YbvWEte7M+AodvZWdGK00XNSf1Xof9+6gYQftOkIii2t6avtZI4rPwy6S3KnTZSTDGecEibNCLCspbfSubfN4UV5ItEDdg/DNj/tyKbESDxccdf6HfrOFp/V4bWrjlVqXHc/q+vubVJATapMXzL9ST8by1nI0UkzBZKP+rM920+wObqpQP2K+oalkX5nF6YRpMRJSB1T5hp0oooZqasACCv16Xo9tBDXKREWbUZzWzi6i2HWXCsJYUf9qRHbE90M8inof0LobdWDf/bqoAonfdSlrhCVWM8ET+TTnRlWd8EcPp7/KvqDM9AKALzso2UDlZopRX7pVEsZqTZgnTYScuPolKrIfvbB9nrEreM/XYDO6f0JmvWGUMN2GEn/ZYNXE3gIqpeU4Nql1S3UdHLQuux+UKYLYZB0jUm7VmDQxrD1RHD/wqKf1mlLmny0H7ilCjSJ+9CcwZOe7RbY4yOLrv2bwhli18jr9LriFOiWFxexMsnMqjRA+cgM6MOc1O8ETmlSRNONK779wQ2vBNY9hN5mTT9MLTzcerSLOLmugIo6hlUMJ5X2pyxpFeuCVQDBpTlG8j42Ojs6nfM7mAJ6XRy85jFPj/4K+6qGal8/fMcQGVE3o2g7KQhBAtuWrR3HQ65mlO65umQpc3GoEfGsvo178MpunOKa6RqwSrMDPv7nVgWrweC72awjejP2agB37nwPjLNG/w6W1/V30jVgM1CMHxiCCVgF4pvns4blR05NvgkLRMTWVC2lj4vjqf1OyGmAushrNEViU35sHwQozO/Y0mtld7Wk7RWLWy2O+httbKsDuZXDmLLIz3puGxjUGG43wvCZOL2TX9jZd8PiREWVv7zTawTjakMX4XRznPvIf3htQ32OsE6AE2uQRUK3RbcQ+ad+SEKImg2zoEzpweicDvlD+Sw6RHDW1hZp/N8p2wQxxvu9XQNadf5slsrhKWSS1avNqLfFBV0jcXlRUErudK7Ky+3/l/Acd89tJrUuWv/hZQPPAMEDs2mZ6RTuzgV9fUWRO8+HnTIbE9qSslfTZSNMjgJXjzen8zx6zyKwtJhJ+uBIrSB3RhWeIZl/ZsGzpkJgZrQhGtX72T+ZW2IP3zU3wMVwhVKG/guqkb14eDFkrKrZqBJC6f1Om7qcwXy5O6AOXKkzneZxjYy7URFQF59N+i82Ho5eRePRineGlBP8+65Hzf3woPNtgWcDwb3gia93Avp399N5ntrIIjRDWdwAeLnB/dOd03rS68ue6m94Agl17/D8MdyqPu+HXv3tPSoAa9c2p3Up9aQ9nRB0Dxqh2cz8F/r6Bmz39NJtFzVlNbW02TFHOG86N10scSgyUI225082f189NqdQQ3f4vIiShxVjO8w1O+4sFrZNaMbWy5+h+12Jw9dfSdy4w5+dNkb6XSS8EUhwyrvZOms9wBDpfvL6lbM7pWFXleH1bQhrFO0/0anx6FrCGah0vmp7QGdra/qbyRqwGYhWFyTQvP/roYZ4a8Zo9hBCE7mZgFLOdndSesIF+DCGl1n2V4YBnlko/TuinKyCnnyFJhMaMdPoCYmolVWoooNkUd/nSOkLmk6LQ51lqDAZuehB+8l+pt81KZNqcjLwlKlE/31OjJM64O+zApnLEe1GmMbGHBCE+x3JvD5iRx+3pWJuSyKtFkHce7ZR5YpUL7dWxCJc+9+2LOPReVFrLOtptP8e8m4d63LeMnIn4UrnbTZ2JQNZZ/1ZO2Qd2iiRNHpy3FksDbglJMvBV8M8MWwLdcSM3J/gHfji+gR+5ADmvDgou+4LCb4gliBzc7QTX8l4U47zoOHqK+wLB12lJWb+LVnNM6LutP2hRLOT9yFik6dNPPt4Z7I++OZ2+UUKIcCGnbx7D4UXzwV99KCb0f4wely1p9JpcuM8aRO34129BgQOHoon9+Fy1KMva6a1Onp2jjTwlzNmKZFrKpryb9e+hsFs5SQw9jobwvIWzYMy1cWZqd9FVQN2CE1fqyNZtySW8m6dyOZWqFfWconDCbpjfVIh52Wq5pybPBJ5KBeAIjVG1FbtuTkRWnEfx562ihjto39PTvRgiMM23olFsdeav7X1k8NOO2Xk+hCCTnPKV0tun/UPjQZzQm9lnFtfuark32Zvut8/rP3Cjp8r7m8TEAE580YntKPI3flsO7p4GrANfpybiy5grpLKpGOwAgu6bBjXbKR4aNu5vUvZvBIvyvQKk6CNOpvQx7hvKHvYBUNB5k4pMbo3XloVRE4OmFgqlEY/vyjNH+v4enRice6MiZxHZbtTl7eXE3exaPJ3JEf8ci6UQ24EY1oRCP+RDTy6TaiEY1oxJ+IRqPbiEY0ohF/IhqNbiMa0YhG/IloNLqNaEQjGvEnotHoNqIRjWjEn4hGo9uIRjSiEX8iGo1uIxrRiEb8iWhQDVgO6c38Oe8wtbIHw+K20deiUmR30k6109YUx4enW9DGdIr1NZ046oineHQy2r4D/GD/9JzUgH2htCkWAJcNeF4u+vojT+QZgILwkwhR60VVWYWZS6+/nSW/PiUAzv9pglzc7XNMLqrKcGJ3Nbqx8bxGOrj6gYdZ+cWjAqDPuMmyzQ/lhvaa7/5m97Vdx4TJROnHXQ0eXBfc6qI9HnpdtpkSmXpC5fcZJI70hrz+qH8eoAZcPmEwmx+aRtrPt5N+84aAPHzhLsPNa8fII4NOGwcVFWEOUw10iXTYPRFy9dVa6y7tRc/ni9g1UAbd8C6sVrLX1LLy2YFELyxEOp1+echBvfhh3mwPT8TIIVf6Pd+Ttwxi7cvTqdRqGPT+I3T8V4G/onC95yGsVsqezWbNra9xz76RnBhSGRAksri8iC4zxrPz2YcEwJBrX5XLp74bVg24Pmp0BzcUj+aHC984azXg+nDfS+/xk+WY+7/jm67NKX9sMMOuy2dnPwelLw+ix6DdVD3RDnVzKY8XreSVUaMRJ8/w+PIFvHzRKBaWvuZRWD46bgDPPPAx18adJv3TsST/pCGkIc0EIHSJ6bEj/NjlW4an9PO8N181YIhM01CYLZy+JpuFr71OYrsDQZ/FgoMG1aUTzdN+g7W/vPOvZtGuV/zq1/5H+5F/zxsRqQE7pEbfyfexZdJDnjwe2b2V3taT5L48gTazinBmZ7JgzkzMQsUhNfKuH4OpcBeH/96b5U9M9kSruW2P7VAnOa+qBe9ndTDaQHJfFIuZkn/2If/mySQo0TikxhVX/g1ZuN0TTdp1+nh2THwoKIlDg4Q3O+5WqJMaDzfbgYKKE432JgeJrqidm+KPogqFbGshB5wmJiSNxVR+uMEHdDaQBdvovOIWNg6Z5feyHi0/nx+KOxMV5aBT8xPkJO6hX0wZ3S0nSDaZUVd7SV6EkJ4XrqOT265vwHXcOPTIYJpedgi7ptLkiDcC7FSmpMW7XmKQjuuiOVYXKB2jCMlA0x5WfJhNxm0b/aKgPKxeioqa1YkFSz5nTZ2GEkTkqULbzpNfX02rK3cE/OaGPtAgSJcnvIxJDUX7zExZyuV471/r3wWhBbcZWoyJK95cwlGtmtMLvTImwmTiwJwMOjxyNKTBBSPariBbJTbtGEe+7ETLq0v9fherDZJ8NzNcfeKVxE/zyf2oN+/sXcGOO6aT+2x4QyBtNubc+AZxipXyF9Kxku81uIqKUFU6/TCGzv87As8ahxe+NQWIwirMvHg8C5tsWMdtfmkv2l29zcMeJPr3AF1HFm4758io1isqmTz0EtLZQNLkdeycZNSbtKfzqdYlggp0qfNSWk9QdoPUXYQxPgxhukabmYVM3/oXrv3fLNIeWROcj3kJsC+wrhzXqhn16CMu9rOGeQQW7V3HvKodvFXRh2fbBf6esjbWEzqtS+lhFtPRPe3RJh30m/kwHff6E1RJTSPnys040KjS6xosC0D7uftgkvH/kfsGk21diUNK73uSUKXbMAsFh9S97VE3uBjAuI6bR0JBEKVE3hl7yh6GMyds7dJOnyYxsYoWaiwAR7Vq/rLtZg5ua421QiHquKTZDhtHH6ilal8Too6qJDurPbIx9RFJz+kLN+GNUFWif4kHH1Xo3KQ+IBykKwaZix1Y7oxiOb6igMErzQZb+FmVjqNKqaiNoenV+72acIDicNMnmih5sT/vtZnE3aPuQo8KfIynhSBDcwSEnSoOlzesCHaMb0b6p2NJf3x9SKPVitAGF+Cp7ouMf5raqRlp0MnFrSxBOx5c1toPQqAmJrB47gcsq4MKLbj2WJ1uZujKcS4FC+NY+QM5dLhvDwemJtDmKkNnqvj9Hmwe9m6AV+KQGlk/9aHzHQfY9+jZqYq4n8vYevH/4eBWjbYuzPewvyEEJa/0J/3JQqxlVrRdXvUJPzVgIQz60gaQpOzyCxWX+ZtDpo0Ue/7SjA8Hv83zZPvVh6B1w7de1atjus2GdZ+XgyJU3eo0/+6AUPPLN/+N6DsOsej19UHVgKeebM/yygx0KVCEJGfDaFo84EQr2eMXJS5MJo7f3p9vkt/08Cec9/g9HM2RKC1sDOy4h8uabeGUFst7xYPp8NyqwNB7XWNq8hLG7s9l+fZMiICTPXO/13BXJ0ly5j+C0KHT5lqkw4m5vJK+8x/G7fp3KT+E5nDSYnMtQ+c96uFo2HOvN88ocfZGNxzCE95YrVQcSuCUXkuCEk0rNZbKJW3JeH29X+x326V4iFrCxfv/FmhRoElpMC6BS5ROC5DYBq/sczDoSKpl6KFK8ZsDKOj0OolqDMP1HL97ET5ZJm6FC356kKztm8ERZFgdIn/V1WFLXdJl8mHyvi/km0eaB5QfGvZYRd9u3BRvxMWXXjoLLjWO95o0njavRy4A6ZAaz947lqgfNoRuoKZtfo3CEQfOpOZszPmIXIzGWZ+03A2zUCm9dBa5qbeinaVGpjBbkE5HZHVKURF9OgOFnNINwUWpS4MuMS6OxG1G+Tq9WxLah3PVq/CFEkinf3mUmBjQdfS6yDyyYOj03h5ut95DahCqzLOClDhL9zSYLPPBQNKcplfuM9p2efBzpn10Be0nF3jIjRKpINjTkk4n6ydOBwyDm9uuN02tG2j6OaBLjjkdfCKTAWir7ApZxhjFwuFHUumyrRgZpJ3Vh685yPBR9dVr65AOO869B+j8j0rPcWeVof6irNxExjofc+hjdM2i4eueDcIaXWmzkf6Jk4TLvQQglvOOUxbfF1ONwNZMkj7nDGVXxxNfBnHlTmJW70Y7dfp3LSRSp6qD93HapIOK2wfR/OMCo0EKBeESnZMOO6dG96PJp2uCZmUWKhdHa1wcgmZSk4WoIrhelOWk0Wil00ni7NWsfrGQK6KHodnqiUa6O6AgaFFQaVQMXcNZtpfvspOwjezB0Wwzte2coEgsx020Wq8T82Ug4Y0vdt5hjEAcUmPEzXeiLjXmztoQucGVdgfXlwznzLhTVNzlZYeVUhAXZaPJiJKgnVjKxFXM3LcC8HrHQgltGDWpM3PuNMakDIXnHmq4YK5nuOu/3cm8a2tI1n9fCEVw7J8OTut1ZC94gCzzRo9z8N7WhYxJOYMEnEeCC2RGQj3qkBr3HhzKnpxa/2tbXGxYv8Hobnsmma1XTOHqJw2JGjdtqN//ZwshECYz0mFn8p7VdLNEezzYYPmFkjkCuHDLVbS7ZD9NRsbhfu8modM1/hBPNvdnZxM/J2GT+ViFGYfUOHHHIFrN2+HhEfZDKHZC11pJdVIUJDcgay8l5mod6wIvO6Bb1Vdqgo4fCSy/bkbpmMyOfzRFKBKpCzr/6yTa7jKD9c8WvBxm8fuyJ4af083oxNFu0Z5FDoB12Z+hZ/ssRN1k/DnkrOLlYxfy68wcWk0L3ugjqdTBIDWNnr3LPItlVmFmzQtTOTSxhufKh1N6pjnlFQnomsLG82fwTfVe3v+0Q4P5LqlVUX36+tZqFV0soQX6mm/3H2bkJWWDOI3avBm1/dNwRivYEhRMtdKQ6QmGEn8+Wb2uDuv3+bT/3j9ZytpYVmcNJvnV4BydwmTipYs+B4w5KrX2HBqklOhnzlBzqZ2WeqDasHTYebJkEw9PupuW764J8DaTTV6DK51O0m82+HSFi4fX932rQvFL3xCEIqgdmUPppTPoc8d4Wr/bMFepdDop6DsXiGFE9mYcK8z0jj/EPU3345A+zGG/YSSmIBiRuJnppPsd9xUfPVd0fmw7F+Q/QDNWozZvZvDyfrQGU4f2nOndhuiv1p11nvcXb+eh/OuJi60j02xQsJnatDY6niDPYcH+fD4604bnjnUL+C3qCkMx2ONS6RKlYzJfXdiZsc8W4CuAvqjz97i9XLNQWTnxTazP+9M01uh2+k99kPaTAt+tMJmwXdIHTRbw4+tvNXifNdJB/4UPkvWjvxowGHO1eWsfo80qE85WTSgY9rZnTvf6d8cjSpSwoxtLUF/+3BF+Tre4FPtf2ngMrtv4ljiqaakIZp82dKs6Wo7TyVzH0bp4EKDEB5fTOZvVYQBPM5GSf3f4ChPesam7EU9O/hHdVXkUIYhRopk55koUZXODWm0BcjqKiprWgQW/zg+aPmZtaeDjlxLtRAWWRRVYgBhg0p41XDnsfjLHFwTOt1VXYx/eH8sPhZ6Vzgu3GMq0umt4rgjJsj1N6PifIPNc7vtvn0QP63Igmmqpo1TZI5nyCoSiIntlIvTAK5Vf1IQLo4sofHY6ue9EMB/vWqgSFnN4iZUw8O+Yjfk5xXn2RrKkv+Fx7qM5r74zAjXeQbppc1jDnZvUB7VFi7D5yupq9CCqEPp5fRC6RFm7BbVFc46O6ESz2YayyJ4XBtLxmSCk5fVw/NO25GdPJ3dWb/S5UazIepvLP+xLry/38HjLOQzu8gjJL689Kw3CjqZKWiWe4XSdlRppJwYL9vS2KGxd5Z4AACAASURBVEePBzU0lXot0/5zLS2XHgD8HQQ1sanx/DSjpulnzrDg1/l0+vJuzvvvBHY+Z6Q79PBgj5cLvruB/OtEjGIh+aUQdVwo7BktqZI2zEGUKuojSqh0nE+AGjCApkjP5lgpIEYxY0LFLDSkCD0V6M37T5xeAKjNslGj24lRLFRJG7kbb6F6aSvarKtl36VRRJ0Q6OefxG43YV0XR9KKSvSq4NLnkRASh0J6vW1Na+o00s11nkU+X/iqijaIelvNtOLSEAlBP3kKU9s2OA8bcj3Ja+JIia5A9dl5UKeb6WmJIuqQyZARCmIJbU1VLD4/WC/bE5CmI4SdptDjo2mpGnk4JIgI5rsC4FpIW/DVRyGTaFKnxFkb8nf/Qmm/mci++5TxSBW0KInMqmbneR+iqxHomSkqpo7tgSIqtRrPexUWCx2/kpxJjmp4gUxKtGPHIrpWwALpcmO7ngSch4/Q7P0jnt86Rihh1ezyXeTm3AJiC4w4zsh+YxBiI0UXNef6TneQXBD5tJEbj/zlTmLXbyFWCJ5edyFvJ62lrpWVUOO5Fmos616cjib1AJ2v2fnz0YFWPm1OkzqZ71cb26VcRremn3+nFGy7l0NqpC66g0xRELQjkppG/6yyoNzGoRCz/TDOemrAAAki2ivBLoTHDqkoEWneKecqJBgCYY2uqW0bhEmnwmV0E5Ro5GctSf6iCL22lo7LXAnfMHYYSE3zeJ3BcK67F8BrsDWpc0Kv5blOQ0KcdZbwLa+r5/eWs57OltPJiYs70vSz40ink5kpK8i7eLTR8+vewuapWaTsDNWD++/pzZo5DtOTAt0Kai2YaiGqQie23EFU8RFD4RQCdLuODkr0VP4z0oSojtAwhsD54+8idsl2cPiMRhTF8Orc5Oz1UOaoItVsTBkIk4niD3qw+cLA3QtgvLda2bD3m/TyKkMwUAj0ujpS372TLh9vQWtoUVFV2TEx0ZAVWvgAWSbXfK6mUdHZTJs3vO8j3EJrRAjSsaiZaaDrxir+OU5fnFqQzpreH5HbrjfSbkes3uwSqjwJG85tnUS3qAhXJ1GcYyev1WXEHAm9VuCu+8GmAluosXR7ezztXyuoN8fuLwc1e+AsTD7eaah2n6mEET/QNaZ1+IasWY/S8ZlIFxb9PXNfVd/WchU6Rufoe1yR4fe2ByuXe9okHExhmmN45YhDh+nwYTLJF3nn4irzqmm+MNYQ3nMbAqmHnYD/rRBW77SCE42RG2+nmVLyh0i9N4SjAyQJH3sbrKg8jXQ4gsrEoKge4UoPpCT+szWoTRPQTp4i9flAEUU3nICamEj5zV1ot/CQX74tr9/n+Vqjm5E2u99+zLMxKg6podglsrY2xPxx4HTBvucGM+7a7iz65mPvNfXwXsMVY+7l8HMNb3KXNpvHQGbdswHNt26F2B0jHXa2XTQDq4ih678O4XRJt0hdEjf8MLzhTbvrv73IuK2gwXKcDXy3oJ0rEv9STs5140hktf8uikh2VISAYnN6u0spXQocQXCu6shBoKHgOzipGj2AuC/WGVLqUjd2lESAFmosaR8dO+cZVV9VX+lwenZX+R13C0qGCBLy9fbdnYdeV0fqE6u57olBPim3+KVrxyp4OfiCcfiFtKYJ7Lpa8WwZq9LrcNpMlDyQTszhDBJKHNS0MtFi1RGqs1oQs/8M+sbtv+sLBDh8d180uRpVGNKLd6atYJ7eKuw5v9mbCZFn80LvENV4CUeMueAWzRFWK/b01lSmR2FrJrCelLRadgy9dF9AXseu6UqzWas9xszUtg3VvdtzKtXM6UG13NPnV8Y23UGMYqFGX8xt14/wnLv/yQFs6zzN8z3dXIc+x4Im23qO/djlW8//kyrS+KmHV4LdV7kVh5MnDvfHef8Jjo71Bj+4oekKjvxE2v/bf2hrruL/Y++846Oo1v//PjO72TRSIUAIJJAQOgRCBxVFDYIF8YoUsVwsVEEFvdfGtd2vFcUCoiKgchULcFVKALFBgEAgdAi9JBBaSEjb7M6c3x+TbdlNAbn8/GM/rxcvNrOzZ87MnPOcc57zPJ8P6snzdMocTiP2GM+nmmWYTWq0/W00LbOPoXRJ8nlOdfB4h4qKEhSIXlLi81zHasi5OgDQNUIHVLqMKg22tCqGJHh18KUGXAUOBeorib0ftuFw2izS5l3airAmqKcKsLv7uHwMWOl52QzqO7hO5WWPfx/zBM/nU6pXcNTtWUz4YBwbnnBlkf367kwKppfTY9lkmi4XBJ62ohZbsYcHYd52EO3ixWpnvMt++a5O9fq9HCNRxA0HX+yCFiSNON1FVtQNu1DjGrNncmNXnO67J9FOnETr2Y5DQyweWmpeEAJTszhe/fU7Zyy4Y5/rUlbxNW+kXShEDa8gXDEiGEKVQPbf+Cnn9DKilSD0yjHULFRePNOWW8OyGfHlJJq/5HsWcbnRC31HZaEjUSuvNTrsBI/kuQIJNaljR0OTEh2dcqnRfc1EWt6/5bKuVx2k3U7U3PVo/bqg/r7NmMVWhqvplbvXSm4e0b8Z5wuTieJbu3DmHu8BImpeJkqnNux/2oJWauK/N37AHb+MhzI7yaO2kk4Y6R7KuueMIEQheHB4ukdZ9dUQFiUvrnIFl1GZGnWQX5vd5vw7vTTcqJ+qohWXsKuniRDtKN7e8UroGvOOreWBZq4EhdgZmRz7NpmmU8rQK6MVqlMDRgiSEwo4+UkkTYdkwquu4746W9UMKqkbirFly5uxot1CBj4y3uv8kttSscpN/Lekho0wKXn/6DomtQlGdxMxdFcDrktGmoLk24OdaTLEFQdqim8KusR+Ivey3Qtt/nGctofG0ZQMz1WS47NQvI85PntU0OVzliUlxu/c50E+Voils2uvsy/D4p4G7EDj6Rn8Y0Qf3m5sbB6ahUq0EsSuQR9ivlV1puC2+vlhWt7n221iuzGVS1EDfuD3sSSbd3isyEJyBaufegublAzc/xSNskzYYiPJGjLdFb3wzTjU/DOc7RDE2rveoJ7iePc+FIGFglY/jBSLhc7/HkdRog4SWn5xEUTdMxFrbl1CELUyCPrBOqtCiKgg3mSjXEpUoVBW2Vit0kZzy2nMQifpw0PYL3Pnujp0Dj2Kjo5NGi/QMcLYqyw8VCFQUZ3B+LekjKix3OoGAU3qHLGXeqmgAqCo5PUJJGFLCAQFIhtEUdg+glO9oF58IS+2+5HBIe4biZs5YS8Gqiw1dA19+16SHgxAWq08lTqa5Kws1Ogohu7N46vWPnIqAaVdK6ZGfe11vLrcdE3qjDrS3xDTrMSHLZMpXJaEaU404VvzkWo1G0xCYG0awcefzSBQKBQsdYvjtdtpMvQAJWkpXPPdATZ3C/LpJhHmAJLXS7Y/H0PMEM/kC7VtMnseC8MqN6FJiUWYyH+sNw03XHSmZ1qjAymfdJ71nb6nQMsgWAn2iMVECKQuafvP7ZRLO6/MHkmsebMRvw0eHeHxA3tINod4RR/cMnESDu6FJ6M9/ZPV4bGozQxfc7fzb/fne7nY/3giS4e/ycSX+5DzQSqzbp7PO0ltOPp1W57o8DPzp91OxLpjLNy4iNv/PgFLfgnfL53PnX97yKOcnJmpbBn0LlZpZtnu35h+vgW7imMxKRpBqo365nKSLPmkBecSqQajSZ1Frb8C3rrkOq84mokmN2CVnoY/p6fk9nb38uC3yxgaWogqFFREJWeKjTarxtDy79Ub1cMjRJ3VgC3CRNPvVKruWjd8P4MtkyJIsVzwiF4IVSxOw+/M51GgnmJyUwquGTEfrjdC5KSsXsS2GtRsdKUk6stNpH2eitomCT3nMEpUBCLQgv3ocWPDQ1XRS0spHdKD4MWZiIALl1iF2vFNm0Z8MG4INz+cwfjotTRWg/ihJJIXdtyGsiEcqYItVFIRrRHW+CJdGp2gV/hBjgyOcJZRZjOTr5URUAfXhyYlN66aTDKbvb/UNZq9nknB0K6sf+sjsq1WVpW05dNdfSg5GM70eSP4+I8DXj7eVb7iuaQ0NiQUlfKYYH7Ny+akvZgHh4wBZY/PGUnBG5e2pH3/QgvOXeM9mwi/9TCmeBsDl25hfET1BsNYPoXS6rOxxoaG+0rVVkHQii1s+knFdmMHYl86SN+I/ZiFhk2qrDnfmvPPx7Ov6xYCTd7ZbvaIIALzzHSa85jzmIiEI7e7JVxoArGhIa03jUUKiPu1ApPi6qwnF7fmpqb7eLvxeiCIW+9di22kywAEqzbGRG6ksSmUYr2cfjuHYVGOezzb4P9uZuDvNxC8RGFe85/q1PG+LG6K7J/nTH0tvbMHik0SlJ6NGteYI8OaEPf6RoQiODC/A0n3G2nCeZO6E/ffk+hHc7Fd04HSRi6j0vLTUwxsOoFEtpL8eTljuZ9kMmk0L5C3rrmDxB+2Yi8v5859d2NeuRkduHPfENjgIlZCCJI/K+PaA1OIvjkP9bVoAs6XoQeaqAgPwFZPpSRGxRoJ04IlphJBg202LEs3ebXRuqxMNakz6I77kFv2OCXYwRiU5Y4c5iQ3Zw6gpLTl4D9MrO0zix7LJ5M8ZkuNezLz+33qsRlXE1ShELgsy2fkzNtJ7cif2BvVJj02uy8LUkc5cYYCrZT0XM8NuAHx3eucOelXA/bDDz/8uIrw8+n64YcfflxF+I2uH3744cdVhN/o+uGHH35cRfiNrh9++OHHVYTf6Prhhx9+XEX4ja4ffvjhx1WE3+j64YcfflxF1KoGfOCLzsztM5dOAWVc1DVKpSDZHMK8ohii1GIChMaKwg48E/Mb0UoQo47053y/ElZaF1wxNeAv9veUI+udY0WphQHBBruRQxlYqcxvdOf8dQjhuavXXgk14OSX35EJL2ciNc1QqnCjCqyOtKZoeSLhtx11qiPvOBYnn0joZaS6qqqhtltLUHXD9WEcnN6G9QuneKkBO3DLrgtMjjwCQNuZ42j6ijcN4F9RDVhtm8xXK+cTLAJQEN6KwEJQNLwHv775PqW6jUg12KseVa/55pENtDMHGEH7m3f6VANO+moMhx5/UgBcf9Nr8ucv5lyyGvDf9g3jlxve/kupAX97IFUODrng7A9g9JUDNjufF/RiT1EjduxrSugBM+GHNMqiFLL+NYvEr8dwePKTV14NWAjUevVYtvd35yFN6k4KAUf9puX3Zmeq7tW+/qwa8H37jpMWfIzui5+g9XN72DczkW39ZhEkAgzOhCadEQEB5LyZwvYhM7zUgG0nE6WDW8HRbv45cAn3heU6s9o+vJDIipQYpKahtkli2aqFtFj8KEfGTrl0NWDZuxMNl1q4tj9o0kKom50aWe+ks5P0sPxOpBpisFUJyeEXqlfavRz8XpjM0NC19A8qhcosFROqR8NywEg3NEh6Gq53kbxcCTVgJ6SETq04eW04Ra3sxCacpVuDYyQGnmFg6C4SK+kOjQFgEx1edmVcmSu5d6WmcX5UNwLuyefuplsYGLqLONXITrII47Xk2MoJEDrLittxblME1aHsju5MjvzY+XfocenSWdM0L4P+V1IDlkdzef7UtbzZOAOzg7DGXRFYSsK/2czt/+lG9LpIPo5fjn5dZ2pCxwCj48jNO50qFk4oKvce6Uer9/OcmdkL570HGO33ph0jKbXW3MEVISneE0mLf7gy9P4qasAfJ7fgYyB6XSS3189mbis3BRUhEOpZWonzRsqsqhLU0xAiaLGoHCZfeTVgoaoE/GjIlDsmOrlaKbsrotEQ5NqiWH2uDYV9vYVU/6wa8LmHe5EWbKgBC61SaksTXNTtaIpEddP0E5XHq6oBV7UxUnhrprmrz8jKjFdZAwdvzSxjRVYuJIXw4YWmjAk/6qxAl5fHUhFmsGid725DmHVCdgRSGquT8KON5n9kwbPe5V0un+7RsS0w/+ji1NSkTo6tnMkJvb1+I0wmtF4dOHZzIIlfnatK9QlcvhqwO1b8uIDnTnfgP+t6U7KsEdkHo9lVbGf5ya7I3FMeTFjNxQZ4ukoBUrLp1VncktSb5WWRLJe18AOL6lN1Cx+86PG3NUpQNqBSGXjrCey51agMXgE14LyZYcTcUbsacOs1KbR6KNdLDbguTF2Oc85dU8Rdek8UquFAVVTUpAQgm4O24srfes5ehSIIMVk5c8TF/BZZSZR9V1xPwk1HCa+1RtBQKH9ZNWCAuxpkYavCh4CsMjhqGuZdx2g7axxN1xoroyulBuxe9yUt0wEVq7TRb/swwm7xpMEUpmqkjv6kGnBhIk414MbrJLrVSoOfLfQ9P8UgNReSlmSCptF4naQvvtWALwl1cNjWaHT1nXuJPxDII2OPoFZOpTtmPIAaJmjyegYIQf3Z0rl8E+YAgy/zClPeyaxdHn/b0Rj18pPUN3tz0UrNUPZsvkH15GF13NOfUAN2opLVaVOKSrJ5q5NoQ+rSaHdV5SJqmPn4kn7xyb5VTRlqUnPe7/iVx7FtU120j+3eH0fcaydrrMPlqgHbmjVga7f5dVIDPth/Ljcn3n/JasDgRtNZC3+yUFX2PR/GWa2Em7+fQkvzFqTN4BlWVsWgXZ+HtNt5sP5GprnN9N1nM5fUdt14PP5SasDA0Yr6NDTXottWKTXV7LXNznd7pdSAAYOKs31LHGxhFmEmfPAJL4KYmp75FVMDtlqRVitR32wleomr/2uVA1Hokixa/ug2SF2u0a0Dt0uNRlcJDqb7+kJO2Mtobg7FLFRmdVnAIwcepXRID6QAe5AgNLeCongLwWfsBB29iNy59zJrXD1+KAnmluCLmIWKCZXZz83gmTndnbyoThJ1XcOQZq9O2fPy1YCd0DWePNmF0iHdCMvORzt24rIGmrTYFJTAQCr6tONsRwslsRKpSlq/l2ewVtVhmXr07sa0MBUDoRTqZRywqTzT3DWbjKtFGfjPqAHPu0Q14C8XzmRU0z51UwN2FiooHpxK+Noj1YopOutsq2Db9bMIVUJo9WoOmoPI3G7nrkZb+EY0BimZ1sK3a+lSqUe7Pj/WVc2/mBrwjyc78FnyAj6nqSf1o66RshW+/aMHyU9kGcQ0bhOXK6UG7EDRGxWG2xHBQXuZYayrofT0whVUA27ws4Wob7aS/2BnLvS0Gm1VQNKobNcK4EpMFv+00W0QzcrXOvLi264Ro1+QTs79s9DuM8YUVSic1UqcWmWTT3blh3U9fJZ3uXy6AN+c6U5a/Cqo9OWmBJhIz8umefpoIjZbaPSHweo1adFi5uX3oaBvQa0v9lLVgN2xs5sgWN/oVHfQW8RyoXU9on87bizn6+jX08vLUSp0hB30IIOfc2nGD8bSrpbGqQQGEnNDrlNl96hdMPfsNUDtcuUAf1YNuPElqgHH+NCzqxFCoEZEsPa92bRZN4pmd+fX+pNQJRBN6h4sb9dtL+OzaYOpp26+YqswTepcbO72919IDViYTBzd04i4tkGcntCb+tmlBBw/h372PKJxDK83XMy34V18KjhcKTVg43uN9Pb/QcHYtGpuCuTFQ1lc0IM5ba9HtKmYtw6nEZB2wmsVc6XVgPuen0L0kgAu9LSy7vr3CFZUVAR30au6Ii8L8s8aXVlQyJQXf6Ntxr1s6zXfuVv3S1kg5dJMF8tptlhj6BlY4owsOFEaQcsvS31Ozy9bDVgILlR4CtQ5OHUPp82BNJzcm8V6ORMyWpGkbqq1g12qGrAHdA39ms4cHmwhqvU5ejbMIdpcwrQ3dzOgWVefG1hV8diBvQQKG1Pf7EWTRUdoeDIfdI20iZW+tFp+X35de4bErnL+fcQWxZqjLWnqy5FdHf5iasDgPTjbpIZmr8VZpqho13bCJrP4uSyYgqUtWdPpC0KVQGxS47dvgmrlPa2LGrAD2rnzJMgN8Izx919KDVgoROxWKB1SgT0IShtbKGoex9nUJigx5dikBkVmn8KpV0IN2KiDQLFYPGgyFQSpFgmUAqWYhcp7Nx3FJ66wGrBDmFIokmBFdUbLXHH8WZ8uMdF8fO8d7Fg8H7Vy59Eqbfxj9wgKD0YiFYhrnU+Qycb+4w2RFQot51Zg2u8tTwN/Qg1YSqIsnhIttkrNqIO2ElQkieZQp1GP2Evtyq8OXIIasDsOvdaLbffO8BnKUle9uEHB5Qxsex0NCjdgd++MdVx+Fcea6RTkarQXtGDKCuqunvpXVAMGGHqov1OB9bb627gz5GQtvzD8ubnXBVKsWxm7aizJYzMZmjScU9MD2NJ1Yd2eaV3VgMGr3fyl1IClTvAZnYu6Ruybrt9EfmW4icx5Kj275HDOx6TkSqgBAyAU9I4tgQ3O84zIIgM2qfFuQUL1t3CF1YAxGe9eNemXVOYl48/OdLUDhxFd2zsrb5MaHxZ0QPwURdLH610NWQhayhPOv7VqLvxn1ICvi8hxhnzZpMaE3L4c6e5pDE4+2Ztlk94ges76urO5X4IasDv23zeLlr8+QuL9u33M6urmr7r3SD9EvVAoKvYUHqyDwVVCQjiXqnOtG9/2sYr6hBy8vIHtctSAd1WU0S7AaMB1UQM+rfnYNPSBwr7nnL76z21NWbOhDWFranb7SFsFs++fSaQaTNs38rErKvrRXBrcXkH2Yetlh3FVf0HP8v5KasBSl9TbX0iEUkX2yG4HRaXzq+MobSxJ8LFhdyXUgCsrgfmNs87JUe+tw2nwtNvAJwTa7hyq7SuVasDJ86bQ/Jk/rwbcUm5EA5oP30aacAs5/JPtQkFw3u4ahJQS47koFdVP+moRg1IpfsXVUcxCZf6+HjT9eD1K+9bojg0zR8Wr/n8FkRp4FKVS+tgsVC+DCxD73mZGv+1DYucKoWroXYsR2XXy4bhjwI9P0JKNIBTyn0rg6BuS5iNrn8l5VkRAy3gG9vDsGHnWCCJzLlM11iaR5VavEKvqZohHXu3F1EGtWLZqofNYTWrAqlB44G9jOfKq77C0qnAfyE70KiG6csZUExwDkCPO11Hr4fMeJ968+U+5PGrDX0oNWOqIk+d8ryx1jZhZhqLF/1S+QEp+Sl6OI64+6tYctKpabrXYifpqCM1/KPHQfLsUuKv+omkud+Ml2Cf3iWJabApJbGDBE3EsIK7KmUbb0vbsd57HY/hEjWtwNSoCfZ6ne3xyuzWUDulhGNwrrPpbE+qrhjGwSY1Xzrb2ec7/slMBWBtoSN0IjUv8egyHXu9VdzdGJVpO2MiNOy865aCndfnJ+dmJWtRoTQnNiPzwJDNiPWcAeaXh1NtRx+WxLziEDqv+84HgPIEoLqXF6r/XWqxV2kj8Zgym3HMEnr2MNlNDPQBDpbWp0Qm2V7hFD+gapoRmxP+7ZoOruTs2FdXwTZtMdVIFvpLY+2EbMv8964qUJQIt1fssde2Kh3V6XNtk4uwjvSjVjciFqac6G+1b1zz/1QUbtl+WwQXIeTOFnDdT2P9aZ4oHp3oJnl4unjywix9yN5Gel016XjbLcrdwYVSvmhWm3VDzRlqjBpQNv8DDx/swM+53zELlh/xOhK7abcTDXUWpn4aqsYy1SY0Fi24g3pT5P204vhCzvrLhKILW75xg6cafSHv60uWyV7evx7mHetH90a0s6JMCygXUVi04eG99TMWC0BOSiC+9owUAEIKDr4exLeE7VLeZTKleweGCKOKKC2q9vmPJ5zETuMRX2XB2Jvu/bEvraUXolY1NCIkNjVK9ArNQnRlIA0eNodWJMxx6N5r44ZnwtmdZilu24OVAmMzsfjaWs1oJQ757kqTK+FzASCeuBQV6GfXVEBafyGTArnsoqTBTZg0g4Xkr2t4DdWrnV1wN+E9ABARQ0r6x0y0ozAF11u+6EpB2OxnT3sNSuZG184FWIPZf0mz9UtSAq8P2ITOAyugFphhxuFfAZtwcbGP6+VZ89GMaulkSv8xGxC8bqKv0Wa3JEY3urce0XemYRSia1Nm1P46gx8w0fTWj7vF2VwCODqwiMJXUcvL/COFfbkCsaYL1jViKJ/y5EKH68zZx8DMJ8jxISdP5J9C670eYAzg3KpWTi1sTe/dBrxma2jaZvX2/gEpXi2ODotfm+4kddhi7tfZwsTtGPory8xnyFzUj/R9vUqyr1aYBVwdpt9Ni1F7K+nfi2m/2sLF7KEn3b+eWoY9TFqUQWKAT9vVGkJKuW7PYPCWV+BE7vAZKabXyTMw6FNw2Ny5lOSkE0lZBxsDp1FdDSX79oDM+t64YOnoSa+Z9iioEq9p/AxhB88E/B5AWm+KdSuwDV1oN+M9A79IayxTDZVVyVw9O9RK0evsI9lP5l9VfPfy7EvAR6eIOe/9ULCLbyYOib7/0uP3DwwSHbcW1n1gDXJET5c5MsyuFLUXNSHxx62Ulw9Q631Yiwnm0zzBkcCD26BDaHs1FP3f+qs9002JTUFu2AED7v6JLnuVWVQO2XKJbwImb87n4YDM2dv6axIXjSBIbL+s5VK3/ke5lpO0s4omoQywp2c3HPbv7NB75r3lea2Jeb47eHkGjk3vQ6zgIqut2IPvbKfk6CoAO6RNITt98yT4+aavAsnorGzqZsd/QnsavHGRk1HcECI1yaWbZ+A4UPNuM7M5bMZu2ed3zgS86c7D/XDQZ5JEVln4iC5vUGH3sepzEPNVAXx1Hx4hcZ8xwh5Vn0KVge5caf+YB8+qtDGx3PWE/CT6LX0GwEkCBXkr3GZNpQkad3FYONeDAZVmXvRx2VwO+XJjimjDg019pG3iCUr2CX96bSaFezvAl41Hyz1zSbNPd2N589wOI9TuI0zNqbSd/n7mEll+MpcU/qlmt1QGz+83n7zkjCVC8Y3j/f6NYLydrZVviNR9q4XWAXw3YDz/88OMqws+n64cffvhxFeE3un744YcfVxF+o+uHH374cRXhN7p++OGHH1cRfqPrhx9++HEV4Te6fvjhhx9XEX6j64cffvhxFVGrGjBg5LY3j6fr4v08ELGR5pXCi6V6BTttgul5aZx6UMpgYgAAIABJREFUNZHAVducgeQOVc8roQbsrIeicvHubmS88xEABVopoYqRY25Hw4TqoQTsXo+blLulMJmwXduJn7+cAxhBzu58n457sggT719owfJ2EV5lOM47M6YXjdacdhKdmBo3ggAzS9f/SNrgUbB5t0dQt0cZlUkMOXNTafvyOeyHjgBQNLwnF5IVHh+2hEWjbjDo8qqU4V6HY9N6k/3IDA7ZbD714qrCUYcRGx6WZ3pfwPFMa1QDxsgaS8/LZmDH/qw4/ZGHWqu9b3tav7GLA7185PMLgRoexrANu/j6jn7oB494qQED3LCjhKej9wMG25mTrFsIcmZ14/DthuimTWrc2iTV+3k6LmcyoTZuxNyMhfT+egqJUz2Z8ADSc43Eg0G9XQq619/4mvz5y0tTA7YIMzeO+Dtr1vzTqIdpmLysIH5FZZX9a2cZQhFIu50XD2UxrUUqB77oTODuIOI/2Uf+3Gj4MZqGa04x7Kc/WNA6zlD10DRWad94Po/K+w75vQHfJqaztDSUqV/fT9hhCMm3c7K3iX0PziLLWkGqJYCeT40h8/Mnne3LPTFiT0UpI96aQsOPvFPv5x1bS5RqwSLM3v0V1ztZuP47QpVAp9K2KgSmSjIcR58deONQVux81VWGoqL37sCyhXNqVO92wCY1Or83kd3/51sNOHlqNrKiwtkGivVyOv06llbjDrL3lTZsHzIDizBz2+AHWLnxBZ9qwONye3L4GkP+R23ZgmW/fg8YyVvfn9jgtCc2qWFpfOjS1YAdODW5F/eOTmdyZA4QxNLSQAYFl7PHBt0tZhYkrEb9dI0hcPfyn8sbrwnj9+0hLXgDjhTYflmjiZwdSsCFCoJfO8XCpJ+czEpqWBjaRTfBRkWl6G9dyZj+kTN19sZ/TCb8Sxd7lSmuCe+s+4ZEk4mHwvdSsC2VDZ28mZoujOpFg4/WownB0Zd6sfchF0nJ+wXxqIVlniQq7hACNTGB0/0akfx3g0c3ITOI2XHruX1/I85lNOfbMWmo2TtqzsQRgga9T2IRZhYXtTcOuRFu1JRrPzf+5zqrAUtVcGKSRqmeyYdZ/wU+cl5r3+yOtPm/8xzoi1NWxpMqU6IVFvGfDi0oS4vi3J0xxL3tlsWjqLCqMRMjFwFGym2waYtHxlOridkMmDOKpUvm19jxhMnEoVe6kXPfLG4aPoGkjCyjHDfmO2EOoPszY8n89ywPToaF8w01YID+O+7hYnn1Qm7hQeX80t7oaF9/8T7wTwAuDu1GvYWXnp14crJLZcV2QwqFiQHU/yTTKSmUdP92wEj3rn/HBeAgmtRZ0Nog+Nn3QRfaPFeF4UxRUUKCWb7vDz4ujHXy4jY3ZzlJvpuvVEh7NoX3j64DAox+8LlhbAu0Ug7adMbFuxj7YvCdifZg4g0U3dWF1W+9R1X+OGEycfS57ux8+AOslSzi2ytUVhe342hZNK/GriRGDcEuNdpvGElczj6v8s0vn8YmtToPiPHf5ML/GZ99qQEDTmXhi7odWXlc6IJQJZDEb8aQlOWS/KmqBmzIkfomEnLnD6mprdaeBpzSliEP/MrkyBzMQqXNx+No8ms5Mw+fAUVh9z9j+LT/Z/QP0gjvXbOcyp/h0x27/wBpwYVOo5oWm0JjkzE7krqk7Hro+8NIMjp/hVmoaEVV0kd1jYzphsFwjFwRJs/UV3veKSbG9yE9LxszKi822OUUXXTg4rCeBhkNkLpF44eY92i+YizJf9/sNqs6WH3nk5JjdzUmbvpmpJR8c2I9b57tbjwb5QwtzAXIiopayTNEQAATmq8B4JczyZhCzlMwuAO6WSAF1N9yAX3bnhrLuBQ14Jt3DiNkwCHnOykYnkqb/8sn59EYEqccct57el62c1AzJIcURKCF0hiVyBwN0aaFq2BdY1nrHwAT14x/lBBTludMSkpj5ZS1mzlFcTwSXo1aoqKSM6cjWTe8TdKCJw2D6yN1V9ptRM7fwIYXPAczhxrw7U26EWo65mU8PMrQJQP1LqTnZRPutkqq93Xt1JO+0Hh6BrxlaMYd/ptKbEI+zK5G6ddHCu+0fkv45IYhngd1jeX7/qD12lHE37MTB5uRr2fyUu4gzpaHgDCebalewaCpTxD2/RYcdIXpednce6QfdqlQbLPwU/JyNzkpQ2LKKu0ez02YTBye1o3tf5+BHZhy8loOdnPxFAizjVE2g2NCbZtMk927vI26rvHf5B85YLMzcHU1PIlVkHy4ejVgKgnOOy6aZNRBEzTYKtCtVpqu0kibnEKSsul/nnZcq9Hd91A9ltTfRpZV4cXbh9Fs90YA7JUVS37kKG/V683Dr7Wl5fiN/5NKatd3YXBINo4Z7qDet4OS67XUibptP4Njb0PLPw14fldwfy/AMAi9nh1PlA8lYcfDTotNIX9ib5r8cBzECefXSkpb6n29waCue7AbL8d8yLWTJ9BqyVaDV7cqn7APLoSj33QgfmgGmEycGduLzdYdbEoxRkUlJBglIhxZbgVbhZNYxGsAAfTU1gwNNZ73wdwGtG4SwOf/fptkcwhWacOEyj2HbubidQV1akQvTHyYwNXbq5BTO25cJVQ55tEpznaRRG9SOTDiI9KmpCBMZnI+6UCxvsGpU/b4gT1sK2tGqV7Oiw1mkRabwv63ezrLOP58b+xswiLMBC/eWH1Ov9RZPOw6Hln+lfd3iors1YFDN30GBJM4tRYCe6Hw8KyJNDG7OuflqAHvqigjvbgdUxobf5saNTQIZf4EWs0uoaR5AxCH6zxj/qpNE0LFJo9j7x7JYF5RAvFDa5eFbxxYyLk0rVY+hfN3BiBLy9AvnncpBUvJimOb+ffZizyRm8b8JpXHhUBJbsG+0bOwSrgmewSRtx5w9gc1MpLS3kloAQr11h5C27O/2uuahcrYsZNo/ftupN1eKz1jjWrAle82eaobgU8lz27g6u1GH74KPA813oEaFsa3g97HIgKYe7aXi7TcgcqHqBcX03JC3UTzLged397qVBTda7NWT9cnFLTTZ33K5YTcm4dNapiFSuS82pUlGn20GXuVJXpFVBAmYfjcZj7zHpPy+hL67UakEAa1n6IgC4uMEdViIeepFiQ+6TkD6hp3nDOVdY295wiv3T8KhWyUTm3Qt+9Fd3eJVD7f/TN60nKSZzlnnnYZR/NxC1rOQSbGGzOHd49k0CbAzD/jlvFcwLXo5XVoSDocerELllaFCDe2dl1XKC0KpOUDWR6cv0mTN/D2kQzATdFBcVE0qkJhQLCVAcFGh7JJjd7bKiBlI1SKAd8y2LinE/Za2KSkRN++1/dKSddY+e08NKlz7eSx1Ks6W65SjggwEftm7aQtteGJBEPQcIqjlwcFVn9yHZE3TadX7HaOLKqmdorq5Dx2KDRXVQlWUtrSJiCbyQmxHj/1pegMsD0VoLh2I2+3G5MBYPD+NOyrDRdM960tqT9ZQzt41DXPkZJlq79BkzoWYSZykNEGhMXCisMbK/vyz+hIY1XqQxrIeb+AZcVm9Mr6Xbyjs/d5ldesTQ048j+GbqLXxEJRKb4thZDvNl42YfqloGY+3YRYUi2VnJhvdiRUZHq8HDWmQeWsEtToKMPonTtfp0pfijLwKw0zMQszpXoFf5v3ZLUqAEqHZPQgM8qOg+glJR4k1Pc02ewkde64RbC9S82NzFf59hAVk1BAanS3mHl2SlsUxSBZ9hgIFBX0QhKfPM37R9c5jSGAXbpmVXtzG9HqyGlIaMbE7xdxU1CZx6zrlhY90StstHo6m72zXLLqanQUW7t97fw7pAqr4NeF3XixwS5SLQFcuDOFsK9qX/rmXWMi8eXt6GXlCNV4bu6y9ul52Qxse53Hb6oqJwshKZU2Uj+eTLN/efr20/OymdZgN2nSZThHRa0HFDaWexoIn6jGKKjtWgHZqEIxBsDaiqkD9WVVeBksN+PngEOt4s+g6aRijjRMMjTSfNxvxc1dCNqQg3ahkJMTutPo3QwKRnQj4nMXmX3Qe2c4rXlzn5bd0oXAHzOd4qFS0wz1iDoIqDrgUBUpuy7faTgigaq9veyO7kA2djTazBtPi8pV5VcHfsEmjY1vVShO/7JV2jm1pA2NhuQ4yxAmE2UDugBZHvVbNf09n3UrlTa6rahZDThyoW8+XWE2cbKv4OB72SR+PYakKZu8zrmSqDFkTKsXyIZyDZvUCF+11+Pmr9textDfsnlw31EWH9/IiU9juG/tZuwrm9RJUcIqbT7/+cJmqzEaBisBNFtR7DmTrTSs7x9dx0/LFpC+6HOW719Hel42OXNcHXxrcTP0yi75ZqPLpM6r0jYDck6CrqH+EutkkP8pN4trso1GL0wmBn73JKfHuyILtq00VC+kppE0vYL8W5qh5Z7inaQ2DGzShbTYFOe/oiWxIHX08nJybndt1l24Kdn5WZM6Ycc9G9LKPJeyRutJu6gLmq20Ui89kPKBqYh2Sehd23gx/P9315o6laUFVD6oWpQX4kx2FBTOVeNLrgvyX5MU6mUMO3zD/0zJJH9Md148lMX7R9fx+uGNVKTHoYaHedzfudG9arxftX401lu6GREZc7o6jx/7l6ttxCy8wMjPl3sbQUVF9knh9VkzOTArgeK7e7DtqZkAbHxtFkdfdJXxZvxi/pmb5nX932Z/zLxja+m5tZyC4amYmjQm/9HutN8sWHwik9xF3pLr7pAlpaTnbuXFQ1lEr4skfG00Qb81JD0vG9G1vce9X/viemP3XphpsdjVX4c17c2tTVKNdt6kM+3W3V95nonX2y/ynKwJheN/0yjUy5zPQHSuvo6RShDxi6GqGnCQCCBYUZG1BMdKAYV6GbvveR/Zs33NJ1cDi6ibMkWNZ5n2HadnoPEwRWQEXHARd0+N3o1ZqJzWSrChsL37V2hSZ1+DA2Q2S6xDBesuoPhbcRu6W3ajAmpBqefIqmugqIx4ZQrr//WBYaSQhux2/xnAPwDIKYyBJj4KBxDCmN354Nh1N/DS7WllW63IqHDIP4O4F9gEuVopw3ffx3UNDzjPC85TKO/nchm0mH8CEpphP5aLzNqF9kJrmIfRqBQBQjGuqWtcLLcQWukDc98NvdjMVU+rtKPYPDvpuUKXUN7rTVYwSrm21tWHLdRE8R2S6Zs/cK5uqi7n6xK2A6BWVBq/Wq65s6IevQKtxJiKAE9ZKIdr5dTjvSnqaEUolZtBmkKD311t5674bagIMjcnk2za4rVC0a7vwplJZYZKr11FCImq6pTtjaj1PoTJRN6k7mx/ciaG1pfxXFe3XczMdc35qV2k89zoOTWLJ2pnz2FZfg6A5NEuf3Kzf2XAC4a/5VQ/GwsjUkGc9jS8uoZYl82/br2XFjl7kLYK+pU8jIVNDBg0kvitGfC8UYZFwMGi+lhEiZfxrq8G8WKDXbz42i7nZieAJk1Yc8JqfR4A965/iMRRbhI6eTD481/YWeLSDBsZsRGzMFZBmkV1zuyEOcDZn5QAM00/NKH0EehIFp/vArh0D6Wm0b3lYdJLGxm/VVXOdA/zKXrqQMju/FrVgKuFSTpVgvc/WDfZnaqoGulQ7aVq+lI7e4515To9LbBnWn2SHz3lXJ5dv+NuhjfbxMmKCMJNpUyNOoiO5Jf8ZCxHj9R64eoiGXy5HT5fcgNTR+8GoCwhgoCqfnddo/68LJI7j2XvHR8aMYMI/vb8JDbPNU4pm98Y8xsuo6EEBqJX3osSGsrJBzpwsUeZx2RJ6hC1xuWrM5XqRgcwmbhzxURCB5loknMYe26e837qmY6RZVdQ2rdG7juEWgZfdfsYeAEw5GPO/phM/duMwaL+9CCnoXAO0opK4b09aVzp80xZc46kXx/g0DDja9HbJclTqFdgKqmixnDUZXRj1BBsN3bGvLJmwmUhJXpRMc80744wmRiyo5pIATcU6mVectbBwsyeR2ZS+lAFnf4zicRnXRuW84o8DesHeTfQq8Vybgw6yyySPAuvjK21RsCsa76kb6Ax4E/L782qna7NuMeithGqBNJqzgV0H7589ZctNP7DROHdXfnxjbd5Kb8f+7rZjfKfqvn+pC5pkG3l93LoY3EZKR3J6PD9LP9lsPNce/9UTD9n1VygDxSNcN1L3G8q/SMymdsq3ue52p4DTmMXuHIrElixdAGtPhvrPGdBYWcGxe5gtazn8dtj9mKuW/YEyWNcey963xTm/ucD/p1/Iy3+sb7W5wGwp9+nmE+ozvY+IL47RUO6sPSt6c5z9tliSDYXYUfj5BM24jYIwxvjNiBKTeXkpApnjO6RCUmg7HZdSNeYHb+UlFUTaGXegbRVUH/2etJmV283qu71VFUDrs7sSquVluM3kjbBOL+VuhVG1/4sLhe1mubHXhuPKhQO3TyHsps6OYX7QgYc4sdOjcnqGcLSp27gmL3Y8JnOaFBngba6Iv6F9ZiFik1qPDhjic8ZlLRVkDwu0xWnKxQi57tmH+7xuDapcXZEZ6fvUgQHUdjFyspr3ufA9XPZft1stl83m839PuRcqmu5Yl652TCmuiR5bCbLJ7xhNCQhnP8cO6xy7wGk3cbtj/7G3oqGHnWtf1sO/BxnuCY2GA1NmEyoEeGo9aNB6s76pu0sYmr9DSSOcA1GH3R07eJf0BXUEs/ZXb0jns/mZK+a34dNaobrxG1pVqrX/JtjL/TmrlHjPY5J6bm8F1Ve0zfXpXD8eddS+PjHLbEIs1eCiluBJPw7i/duGUSoEkioEsjOVJ3Y91yGY3VZfQBDEqaambW027n3+aXUUwLIfsX13muFrmH6JZtXW6R4uH76PDOBYCWAZa2WOU81/XJ5el7h325xfs67K4J5IwdV7yZxuz+Hj7n342NIeM7Vzj//z01MjfJWJn64WV+Sx3r6KpW12ZzXTRwsqu+8po7v+PKZ+1bzezlcO2W895fCc2n9ymujUIWCRZjZ2XOB1waeMAegJMWzs+cC4/yzrSHTOy49XAmi6RIVEWBGWCw+/ykhIVy4r5fPOouAANc/t6gHjzIqjwuTyXkubu3D8c4dn/d3syI1w5+v5Rx0fi/MAR6uwZrCY2t2QghBw2/3cuy5YpqZQnnq3c+ZcNsoIrNNNJi1HtE2kX2PhLFo4Hs0UYN563wrgjOPoF1hVV5hMtFh4wjWd/uM4fXyee25oTR7vXqFV6u0MfLQLSDOeZSRtGAs24fPIFgJYNMrs0j7zHhY2ukzJI8+xzjdCAZ3zLZVKZCK2/goBHpogFO996GW/Sm9M4V6aw8ZM+DAQNA07KfPIlQV+w2deLHBHDq/Mo67P6hSyRtzOfFMbyoiJInPb0FarWgXClFCQii+uwcj/rWU8RHHSfrPGNKnuJZ+hff25NpAVwdvalLo98UmCu2uGWczyw8el2rQ21Pi3aNj2ez841Q37I+d4/QYl1vorK3mDSdTGQTsPkHnTcOIYS/SbqPVI7sJPuSWoOE2pDf/6WES22g0zHS9s4gv1sPrxvs680MrYu467P1ONY0zfRt6LIfdO/Fzc+5j8GMza6wrwPiI44CZoCWZXjMe97J9+mUdrh+M2W/k/PXwf8bvXEtnE9J66bveSojrvZ3/JJDrG20nq3Pds/N/eOtt+jee6vw77v8yYCLkfNyNVuO3eT5PHxtmHQMCObAjjpYBhop0kAgw4tmnu1ajaXGpKG1bsnzl1/z+1oc4ZNXd4d6mouesh5eNzzap8diBvbyX5NpnKLm1M398OBub1CiXdv7oWH3kR9jjx9l1e5sahVMPD5zFylJvd+XBF7ugBUlnnG7okiykppHzpnFfQhPU3yqIXLiF4ttSONlH1ElLLffxrrz76Gz6Bxnv+4S9mLt33U/YLd6DnS/Ukv8p0QoKeLT1zXy2dyWDgqH3wBnYBkping9Bk1s4aC8j2RzCCXsxv93TGe1MTrXFXUrEgkc17HZi79zNhzs6MDlqN1vHzuCma4YSlHbY47zodZFOB/7Fa856lZE4dT3P39CTlxtuIFgJID0vm67PjzUaSV20o6SEjTs4+0gv6n+aCbokePFGTx9zZcjJ4Ze7s/OBD1hwMYaYmRnwweNeZTV9w0irLL6rBytnvM/4E/25v8E6Js/oxNJrk/nhbDRJVRI4bpn6u4eRCFUCmRy126NoI2zL1TG+bPMFDyuuyIPVZcbSU6gqWnEJu3qaCNGOEuJWxmY1CBRX9AJ4Gpkm72RyalEiseOL0UNC0EtK0K1WjxG+BeuRQhgD3uc2Dt8RRNIzbktwRaXP5DH8/s5MNqb+h47PTqTZvzI8ZiXSbufd5z+kTFYQKrw7Z5PXMuAxOP9gL6K/9I69FhYLuRNTKdUzybH57lAONeCfcrO4efcQimrISIsKLmNF6/8CCgV6mdMTLTslG7O1S0RpL9emaOQUExkJPbAoW1yzPmH4+Z2rEMfgUPn9De9OJfaDTKhc3QuTia7Pj2XvS+/RMXw0CfcYGW1e2YpCofhv3YBsWs84ib3CeG6O0C2Pvqpr6Dt9h+ytOJqJTa6nWHcbyIVgUOoAlmatwCxUbg4qYZBbeTaZRamuka9VMMYt680dslcnIJvvW/6ALanmvlmqK4xZ/ijJ5q0e7z/8IKx44W1sUrrUgDXNQyW4T9RjRC+xcLKPYO1db1FPcbS9KT6vlZAZRHrcTAa2vY7p9eqhh4ciKmxEnjnvFcVRHeq03aaXlTG6020oiwN5JX4JsSY7NqlRoJezu6IhI3cMJGZMCdqJ6g3ulcCaTmH8cNcE1rz7Ab93WOwK0q5EsV6OWQTS7+GHPRuuG3am6nR5aRJ7H5qFJnV+ffEdQl/27MyO5ALA2wEjJQ0+24TWuwOHxwpaPXce7XgudGrFxcR6mB86xa/tlwDZJH4znqTJ1YdrSbsdFBVzic6dcd2Bi7zWdQTasxfQZxe7znHDujHdsH6bTbBwdaKaNiU1qXPD2gkk6q5G/15Sa4qWJ6LMbUDE5lNItYaZVYCZdl/kUKpXcFavIMGt7jF3Habots6kPnuKvT18xIEKgRIUxA2Z+ay5t4KkZ3Z7GkVdI/S7TVyjjCNj+kfsfPgDcv9eyk1fTCVyj8QaofD0xK/oE6jQ+pMniJ/mI8VcCAY068qmY7O45btrvNKflbAwdjwxE6sUjJo1mSYmb/6Ae+5/jJ+/nIOOzoq231X/LCphB1QUho2ayBpHQMemnbX+zhcsy11Lfm13DoH7jOX08kNZpMWmYP6lEbt2N6XN9DMkLzzOiqXdaLa8lM8WfsjoZn35v7Gf8cLFB51lSLud+vM3MSB3HPs++wTy4OPCWD7cdx0XLwRjDrLRo9kRPmq6GrPI5PpdQwioJtwtPS+bs1oJI5t6qhMLcwA5b3Vhz9/eZ0+FjcfvfBi5bS+r3OJ0tTNn6Tf6YX6d84nzd45Ye7NQaffHgzQftr3a53KxeZAzzre2TXeb1Gj97B4vIdfoT9aTPqUZacHHPGaw7irBovK4VCX1FFP1rq5KHC02xFylzQ7lVhRdZ/fLsexN+5o7EvrUScS0bjEOlTNerR/8w3ItSmgIeSNbE3bUTsjhIqJ27sdeHdfAlYSuUW9xFrd/2w2AE//szfMPfMUNwSfYbwvi3l8mkDx6M4Gmrcgads4TXtpE2gspFN/dg/Bxx3kh/kcaqmXka0GsLm7H1wdSCVwaRsP0YySfyIRHPH8v7XaUjB0krtU4+FIv3h2xgmsDM/ilPIxndt5Jv4cexrJsEy1NdVDY1TUCVm7hyMu92Dd6Frsq1vNUrzudM4+qEBt3MvDR8fz6ySc+v6+KvtvvJnHkNq/jYYOOYIotp9+KvT59gFXRfNk4kh/a7JGaLW0VhPyQxZ5FdqwDu9H8hb30CDuEuVINOP1MOyqerM/q9qWg7PPtc9U1wr7bTNrXKVwY1YvIB4/x+tAv6BRwisUXO/JM+lDmPraRBJO3W8CohERqGmmxKRSObEfyhN0U2yy0rpePWdH4cnucoWS7LptYsd5nerXp12wGtr4W/hvKVy2/99ocdEehXsbIA0PQbitGLXF7rm7lts9S2Jmqo/dNQegSkbENtWEMF65vQb1vNhH0S33KrjOy1w5/3dHzXux2pN1O/923Y+IYJ79KIGl3OdqBw6z+pjfNl583EkU2PUocu5i49AFaLz7goMQwirHbCVi5hbTYFOz9U7kw+SKPt/6ZW0MOUyola0pb0G75eNo8cwRLQa7P5zqgWVfOjepGxisf+FyhWuU63j3fljUdQgDvsERptxO4aisD4rtT/7dg5sSv4rWznfj16T4EpG+mhbq7xr4RmZ6D+lbtbhab1Bh+KA2tuMCn++TzVk155+GhmGoPqKoT5A25dPv7WF7d9ik3B9vQpE6uVkrarnux2I7UqQy/GrAffvjhx1WEn0/XDz/88OMqwm90/fDDDz+uIvxG1w8//PDjKsJvdP3www8/riL8RtcPP/zw4yrCb3T98MMPP64i/EbXDz/88OMqwm90/fDDDz+uImrMSBsQ/Yg8Oa8htyfsYFBYNhOnPUb00n2GOoQbDr3Ri4duWc1ZWyjf7+hMy/u3+JRgr4swpXv2i0PSecaeG+W4iMOoQkGTOsXSyrT8a9jTTXpkOS0+kYkNjWARgE1q9Hh3MrveMOSYp+24Qz5TfwdmoZI25D7I3FUtK1XFqniWtvkWHZ1O303m8GNPCoDU0dNl1Nz1qC1bcLZPQyLnb/DKgjk9oTcxHxjpqr23VZDRyUjXdTyPFu+8LR0SPg0yInBKoTtvWsV6SxcsSzc5n4e7nLy7vDXAmR9asSn1K/pNHEtYxhEOP5TI7nEz6f7PsR4sa+51OHaisRzdrC8oKmqLZvRetMeDdESYTLTbqLMzVfcQnBzY4QZWnJntIcFeflMnWk7bzbFelQqrQ7tRHiEILJDU+3YTSkgw92zax8JhNyJ35nhIsA9o94zc+8967O//KapQKNTL+NvIcSi/uUjmTc3jGbHCGtIfAAAgAElEQVRiLUNDDYWSzhvvo9k/razY838+5b5Llzbl9w6L3YQTKxXJzQGc/DaRbd2/cn7nkC2/7pbX5S9zP3WS6FuEmUHdBmLPO1mtqoKjnfqSHb9UOJ5Hm2ffkRUdSmkxou4cJS03Wdj5Qkd+X/qUALi527/kih8XYJU27j5wG9brThknCoESHMzxiZ24Zeh6nqj/B+FKAIPveQR1aw56aSmr9G+Fe38t0Eo5ald5unkP5z07JK8KtFJOaTA5oTcXh/U01IBjj3o9i0dyDnFryDkswuz9Tkwm8h/pzq/PvE2gMNHu10c4NPwZV/symSi5LZU/PpxdZ9vRduY49r70uIftKdbL6bhokiHB7kM5RFgs5LyZwvYhM5xpwI736pBgd8C9Hul52ViljdubdHO2h/73jua7+e+T+sPjHBk7xSddXI0zXVlWRpPwQp6pn01qgEphEmgFhZ4nKSpCg4mRu3gxZiMxDYo4M9Y31drlYkzEISfBiyoUrn/lCfZ01byM5pDEa/ipJA6zUFGFIPzGU87vdIRTrqc6uXHnLQlDu8ksVNzZCk3llUKR+w+R+e9ZPjukWi6dNHnTGuz2+t5U7HrkHerlGh/cqPyEIiiLco2Fr59r6XWOs57BwWzpuhBVKKhlOvrFYgIqNSy7TqheHaOBWknoInUoKOTp6F1YB3XzIJppEXSm8hzXPf64fbWrniYTx75KJmR3Psf72hBmEwfmd2Tpm9NZ+9wMlr41nQPzOyIrKvi6fTMqooM49FI3D+IVbd8hWt6/FVUolOoVdPtjDKbNnvwd2omTfDHyFuf7aDJkF9p+T6IjB6TdzrJ2buKVbnWXtgpWdPkEzZGu7vbdt5/OcH5++lQvI322BoPrC0XDe16WckXu0y6qy8brywldF3xJ5Sxbn0JItkuvSW7ZTVpsCvOL4il6vamLNU1K9JIS4t7ezI7uKre+MpUO3z3mNLgOFGil9H58DAPiuzOsaW+nwXXggM0gNRrWtDeTE3rXSuN6V2iRJ3eC+zux2xkxLp1gEYBFmEmefMLzx0Ihb0gF3xfXjWAdIGGhq88X6+UU6+Vc1O0eEuy+IDTBRd3u/I0DdSUmrwopqm87NRvdNons2RqPCRVVKKT03+ekuHNC12icoTk7hdVmotGCuknE1BWjj12PTWrODtPgo/W+O4SmMWfinYDBtNUoxFtF989AVKGXMDVq6NVBqsqCqA0aeJzjMIooKgdKY4zvPS6iYKuk+xLmAOYuvtGQKfHx8vWOLuLvMylmCm/rQEmc8VxmNtmA2jDG6zfgYCFzwSzUGqnzfOHUuO40n3SeQ2+GGyQflcrFFmEiWAlw8avqBp/AsZsCaPKbDa23m+SK7qnPpWmKFxmN1DSUC1U0v2rg1QhVAnn0RC+fhquxKZQ7Dwz0om90SLDfOuIR9jpszCWmx4d95b3qqQuavO4i8Tk8OICIwbmXVM6zN/2Xszc2dx2o/K1NqkhFePVXByFLUSJIkzSIW9zw2tne3Pn8KlYc9S00W6hb6sYWKITzOZfqFXTKHO5TyXdq1EGnIol25oxXXd/o8T1T1t1ds3EXAqWewZyn7T/kPNxx0SQ6LppE3++neEiwe0HTaLxO0vf7Kc7f/C9Ro3tB7DlIm86GJMkxezF7vmtNI3uGa4kgBMJkxj7+rFNs7kJ+Pcx31qy3dKnYcrIpejMdUPixpAY9LaFwZKROqV6BKgRbjzSFPtWffqlQqsyQ7QkN4fRZD1pIUznOhn/f0Wsp6dGcoBUupQdLQWUZusba5Z1o2M2GYncpBbjPrKXdRsKrWzj9bQIxd3lzmJ5v6yJj3DXRm1PW2q4ppkrh0Jpg80FraZPe13Mf9csaSuxNotnT5wvSqH3pt/++WQyY1oMjz/pQfb0SEAI1wpDgyZzfmYYmN75lIQxyauDU3OZEKWfd+dqd96X8tvWyVYIvV4Ld1CLB+Tn50wtcbN2IAHGszob3nflDaLpoG8z3PG6TKtYIheqoe7TGVmSZClWMcnZngHpMrUY4ZPxrE8j61yzfX7pBqCqFd3fFJjOxCBNh88MMcnBdIhSD7lMkxgPGEr3Nogkkm71XZ3eFFjFjiYowm7zq6ryWycS5u9oDf3gcb/3cHudndwl2YXGj7qyUYA/9MZtWK92OT6j1Fi8bNRpdvbycjuEnUYVCM1Mo4QNOYvoujuKUWEI3H6O8TRPKGpg5e6GUYmklXAQRUr+UqAX7YO6Vq6R1fxhKDwWzUBkcUsxsh9SOe8MUAmmr4NBNnwGGTzdoVzVNTq+ZEc2mqdUy6DswqO9glOAK6JDscTw64xQaxix16/ftCR2fT1C6y1gFnXMZuGYvbfSpYtBQVYyrS4m0WtnSdSFpNm/DZr/DMOa7KsqckuCmuCYszVwKwOmuFmJr0ZKUFTbuOTiAi2MLOf9IS+fxzYWhwIVqf5fw7HrmHVsL1F1U8vrN51nTYT08V+ef1BnCZObEg22AX2g0NxvdjeZPmMycG9kF2EjUgixPYdMrhcDqOXhrgh7mUlQ+8Ewgg1tnsv07l8S6h/KwDwn2hWPeZkT5k17l2qRKQStBhK/lsVDgohk1wrVCqQv67RxMg2HH6LdzMBaO1Hiu1CWRjxxDR8cszJhLNGibhBZhoSLcxNl2Jv79wOdO+saWEzciq3GrVDx6nvJHmngpkzigKjoTWnzLhnLP9+pTgl3T2D+nrfN4xAYLDedu5fzQzpzpb3VSPf4vUfNM12JBly6KwWXtviJ0o+FoPmYvxowhF2NIcRsGLqVRLkfuSPVZ3uWSmEfuwemPBVh+yNiI0qTOQXsZF/QAEkwVxKiumZ9ZqNTf5ltdeMWPC2q5YjZg9lIndn/n9sNHUSzeHU2vHAik3Ubc7B18O3kVd9lcOlihBwpdZMe6Rs473WjcynM2Gm4ph+tdU409FaV4QVFZ1WUOEMI3hS51Wb3AZSjb3bGXgjdF9bMmKdEvXqT0pgoa6J6++rPVzCrc0djkMrjSVkHSqK3ciUsqPgnPmePT0ftZU8OsWGrCe6ND1xDltXOUSruNtHuNjUN3H6Wjbo3vP+z8/L9AVX2uukLPdvn9k588xfb4DsB2jj/Vnbh/ZyC6tEE9eR57bh7261OwbDuCdvacU4L90SmTafRdBkz3JMkPFHbaXHuIMl/3K3WETRBWr9RDoglgWe4WVpUFMf18C6+fBQ4y2mQgdfBG6VqlnJHhz10z71PnV5rUsUo7FmHy9JlWmUSZmsQC2azp+J/aroYNjd4bH6Kp2SWg6FOCXdM8jvfhMRotCOBsZ8m6699zIzF/ptZrXi5q5dN1lysPEgEU6mU8f+pa3ovdxNLSQPoGFnicb1HshHy/0WdZ1Umse/zeB2Fx1M6LHi/HsYOqCoVkc0jlMe8RPfjgea9jDuypKEX14ew2I2n+/9o77/Aoqj3uf2ZmSxqEhJoEAqnUYCCABFCxgRcUNYp6RbELogJ6Fayv194VESk2VCwoNlSaBVGEQEIJvYQaktACIZC2Zea8f0y2ZUs2odzned/9Pk9gd2fmzJmZM7/zO7/2NfrW3jxsukKwfUpP5Br3QQOGKolOT+c6BVr9osjqlh3I53VF27QTNJU9I2d6eWZVYPcb/Ul9TCeTvPrL/5Cc5WmftgztTYysRzj8WtqF5ug1cbWqKjZaa+lpCmNu0lKGtbgYtdzzGXlAVhDnpSPV03g0o4KU612HNyBkxUNzd7AaB4MI2aSvUnwua4OYrIXg9XYFFFgsulOrHnLTZrLFWuPjQBem7Fvpc0zUhyokIMLjt2N3ZdNydh5oKiXfdychZwtyT52iRtu4HaVlLFUDUgn7JZ/CT/QIH4B9L7qczuJLmfvbf8v0bj3Y8sB0hs26hBGf/cnCoxlo47syedZn/GfTSFp+mMyGSdMZOiWTP9+ZTrc+3mvhzuZSxqbuwViqP4+D9koiZIUoyYwiyVhELnfsG8Kxejb0cq2GZ569n5a/7gY8lQHJaEDY7HX/Bz95Od5XBzSE8/21CBt98m4jwbjLo03JYKQkp6OHUysQIiQTzb/1JOMMryv0r8rCw9fi/ruTZVqGCFlxbjttBNBZAgpdObUT3aZnsXWcbi/Mtwj+/esEklMPcX3NZVTazEw41Jp7MlYwuWUhFmHjz53pdO7hQzOjcbTrHv2o50iRkRi9/0K2H2+LLAk6RR9nbtJSr4crnaz02+YjA69HRNcTrpKEtU0kv3/xsc9jTBV1A1RWMCQmsHfE+z73G/p0YBtneY9oWmyRPJQMD0eB0JATapwkl3G5KgcHRXu0cbKjS0s4diIKd//uqppkepp06WXv1hFphR+hK0koMdEs/HGO7+sIIkzHA5oasHh8INiEyrjiC1nxy3kehJZCAlszQeHohu2IAO+XXaQ7tepBfUNjVtmFINn9av76iq1pcKdgT8jRHcnaxu2u8x87TtjPunPKIXBBN9PwuP5Zu+IYs2IuQNgOM+jBMUQeW83P/+qNKK9AO3mIKcNGkLBXF06D79Yp2EcMu4XkDd5Mvu0Mp/i1pgVPvXUnccvK4NgJ3ZFlUFBbR1M4wchV3TZxzPMwWimR/PnyOxx/0cpdiZ5UOg9sXIcRFRuKB+eZFyTJY/I1SgrbrNVEyhoJSoTHOyojo/zZwkvjBqjobmuQycEdLbZWeDBC+6Ngd/9dMdSd142C/UxAtviPUQhs0925F2u3cKdj6sHnx5M+Wx9clXU2pbSIchbP78bkloU69flRE9a2vsV8Y+N0nf3Yd8Dj+7CE3iCdItagz4LldhtD6cWBJ7LZer/LoRTIsWEvKYWDnvZUSVEwV8f5Pca8emedrVVDVJwCfF+TdWgfTL+tB01lQXUY6sWezqOy4bVEf6EL8MXVuonCQ3OQJNIeO44WEYFWayFi6RbS69lBT/R3o7Mu8hQWnxedz70ZPwBQfHEEHVb4vSQnLhx3L5F/bAObDYxGrs1rmE2iyK4TloI+aRR+3J1NF88iQjZRrVnJ+HMMaXducV7blPJOftuyCZU/d6eRNmUTwo01Q1IUpI4JMDpAR+ocugAr5/SmnbGeE81gRJFk/vq8L3EG/4Smp4OmUrCfuNWl6Zp+jWFMwjKmpnYhav5aBGAvcoVRqbv2eVGwL1r0FWmf30d99DSF8fAdN9Mudx2q1ebJsXaglNRbVZ45sJKbGOBxnGM8+3oPh0fUkvXf+1j73xlMDXBNkqJQfnNfVJGHIskMbZ/lskujj5Vdr/Rmy03vYpaMtH0318tcIWxWHr9wAanLbvdgwvaHJaUFaBu2efzmj4Ld/fcksQEVPCjYAX47DbO/iiD5OwuM9709sKYbFcmrfb4nQtYdU7UtJSSjCWGzOj2BWnU1+w+2JC/VRj+zkZTMYvhPcaBmGw0v7i3QnUz1Xp4OL66E++uxuwZCPa1MQEAnm3bqFHKPLmhbdvhfsssKlQlGYjUVZIUHV94M1xs9trf/Qv8uh4XxwPw7SOlfjbRmq+s6hcC+/wCF084n/aH1aFVVTGvv4tKSDAZuznSF9ETt95zkine2gQz9c/j5ZU6yzECQbQJRa0HYbUiqhubHaeHAvheyuTenB4t/+jzgfu5Y0i+Bfc/39LtdaBLCavWw6wpZQbH5eP5ukBSF8n9nAXnEzd7k6URTFI7frG+L/8hzW300RrOvL5SaSsEeM3etM/JAvVHwboeRIG32GAtO1KdglyQGPTiG5O9yffIoKn+u83YHu0WqxChN0OyDCCEWdjs3TlqCHVX3cbiPPVlB2Kz8dv0bGBwmGj8rj3ujS5n9c5jLqeirOwYD1Vf2xpcJyhGxAjijFPydTzIYPKjXHQg0Jty3OT4bWMuoDgOR8R8nHzhO12Lhh7LedSywEtYY4eX5lQwGjEVmeplkbEKlwhJ2xpMjGoMCHxknZxIVPVp4PDRD+wTPuE+h0eZPfWkvyRLN14Rx0wC3zDBNxbwgn5LJA6gdnMHtly/jaK9I7wcuK6R9XoMS31aPB3bDzilZPNvaZW9tscdTkERvd7X1w3kfc/AhzwB3v3BoI0JDbSBDPPywhHLwON1W3hJU02mf3YdlYFcSfw/ORtcYCE3Q8g6dXFE7dcozAF8TxN+5x+e2xkIyGDxiTVW3JbGvKJRgIIe7ls/7p7fiwo/ygu+jEHz59puUPDag4X3PMR6O1e9512V3e9wzx31KMkZhR2XiwT4Bk0FazN8YONpEkim+RmWj1Xtc7Xw9k52vZ1L4Si8qr8nyGScM+nOtvCaLwld6OY/xhyn7VtJjrazHBofpz+6LAys4dld2g4kiDgQWup2TWLm2MxoCRZJJG7jPy/Yi7HYiSl03LdpcS7t5Z5cVOBAyzWanlmvo2OHMNi5JRB1wPdyN1loq+rf3DEAXAvve/dRe2Q/JYMBcLriuxRqvphJeX41pcT4f/XMhrWfkIndsT9mYbI7fkY0cGanf59WbUEsOsn96K64ZNtrZh5cu/8Z5jTahEr7H02HYckutM/Y20RDFtaP/8tju2OahCdR7z7U6z4NHBpnbs283I4+978SSNKlSH8x198Ai7FRrVizCYf/WYzI7LrFQcpERZWW9xJkGsq8kowGM9V6W+sdoKvPTf6ZCq+coq9Pw56UupEytl2DRBFgu60XR3C51oXK608kB0atzk9qsutBlG0183MKSJy5qVEbaiDcm0f7tAGYNWfFKBvHYXCc4gj6nn/nAPcTSMkwnjjVLRlLesSPcnLTCZtWTfeqw6u0+PicspaXOuqtVVwechITNyvPZP3Lv1lu8hOrGnHfYmPMO/1z3BgcHSj41Wf1kipOC3XGMP3Q1RfBKu3yKH89mx5vnse/5bJrJJo6fpyEFYtV2Q8C95L3FDMjaQa5FYaO1lqKfkrxugGQ2cyLDjkXY+Kkqhp2746i4ONVPi6cHi7BRrVmZc8CHkVJWkDO7Ob9Wa1a2Pu3SEOVgw95l/ZaoQqNW2EF2O04IpBUFHB2bjf2SLHqawijL8PEgZQVzuRWMRqpzKoiQfCyN6pZcnSfULYtm1dBqVi6xs3P5YNuSOo1Tz+Zqf2OhHu0AKLExjIw65uzjgPX/9sjCATCs2Mx7J1KcQvKm6HyP7VePGgN/tOfw3Vl8UvAzlVqtR2q0UFUWDUqm/wYbR+7KIn5VMyzCxkk3T7Kw20n8904qu7eh+2oNVJXU2zYybNLDDHpxAsMffZjU2zYiGQwMKTiO8Wg1Sc/ke5mElGjdBahIEtd3X+8VMmYZnMF13y/HJlRsQuXkohSUNm5ZfHXCwigpDNs8ymOScLzMRklhyPo7/Wo6DUJWEAMz+fnDd1nT/2Ni69Kob7zdzWiXt6lJTYf94jITbXsoltaT9zRKG39x/MeU3erpM3C8BweeHIDUuytyjzTPgyQJ+utmnsjfopi+/x8OP+h/dbqktMBpTpHqKZ2L9+fx+xtTKXXTRi9+eYUr4SZvk5dpq+hJiQqtBrNkJPrzVT5NB4dGBj+JjWp2DPsPrb2yNqPkMKLkMJrJBg8Kdl9wp2APxnnX/qWVpD24hk5P5zY6QCDgKFRPVHD8qlhe6KJTWCeEeduIhMVCl0nbuPHZq8FgoEvZJrTaWvi6Uf0IDElieO+h7HgrjvS4I1RazUTFnEA9ccI1QIWGdLCMtRYrPU0Ka6wmWqwzwZ365lW3ZfKvSD2GVEgg+wpD0lTse/fT451xPHT797ywajjpD66G+z13az0zl4d2bSNj9c10fGaltzjXVKTcTchxbVnd9xMGvPYIG6f4vjSHEFIvLuWjon9ob4gie8NtzhAw930Atr2W7NRyu82+n05PeRa1cey/qHsLfv3zShZ2XkhXUwTVOS4Tg7JiE+JSO1VzdW0iY8kDpC9xo4sXAvXECVadZ8T+sMTjcYuZWt6T3zOaezgYhM1K+KJ1bP5Zw3pFFp3+u51RLb7FVEfB/v19vbE+3JIlPapALvR6+XqslXkzTtfCzZKRl9qsQymVdftYnZY6YdpchkdUOD3ey3rO5aVfXcu/ne/3Iae3rund0+kftuXFU9ALpF7dyfhoqzOJaWzacnL/SaG0/ymv+9Xln1tJbVMGHPT9kDQVaUUBV2y+mcU9vuT2vcOpvErDUOEaQweeHkDiK2sQdlujhObuL13Xkvaphb0902gllwcdavfgwtvo8sMOqAukuWRTFRNjPwWMrLlvChH3m/i7Fl5MdlsyC0GnqYU8cbgna7ckk3PsHtpNXQlTPGN93e3Wl994B4b8bbSszYXnPbeN7HMV9sNHnGNjZaaZq9NuoGKqIAo3hUBWkM7rwuuZ33L+xw/T6bl8XYr7uF9tbijivRPBr1Tb/LATNYC9/kxhRa3Gg5tvpjU7nL8dUatoXqgQLLN6iII9hBBCCOEcIlRPN4QQQgjhHCIkdEMIIYQQziFCQjeEEEII4RwiJHRDCCGEEM4hQkI3hBBCCOEcIiR0QwghhBDOIUJCN4QQQgjhHCJgcsTl8kgh9+hCZVo0tnuO8WLnH7gwzMpPVTHst7aixNKCnJg1bLG0583vribl7R1oJyv1ghY+2IAbCwcjZ9Zdb4l/XpiKWdILizs429yhCg0N4UxH3GCFp5P6Ovsx4NfJ4p+e33sd4wtDb7kb5U9X+T1HG5fLI4VkNlMyPouoEo3mX6120hY5ArzD/2rLj2lL+LEqircfvtlZzs+9DaVlLKNWbmDKqzcQ+3Gufrwkg6ZiSO7ErGWfs9Uaw1vde3tkaPliAwZQWkRze14BN0RVMPlwJgW9fRcud/Rh6d7O4ouybIov0evvAq40ULfjdk3pz+4bZnJH0QXsebErYT/ned4Lo4maKzLp8vQm9g+w+cwskowmuq+yk/dCXyJ/XuvBBuy8FkmicGo/9lw3y+fzeKmsM//kdEPd5SKk9GrD68T6PSh873y2XTMNGZlnj2aSn6k4r9XBBlxSHCciZAUjOqGpA+6ZRu61oFUhsKFySlNJbH/wjLEB9xr7lhg1fglLejSndNIALr4hnx19bOx+PZs+A3ZwfFIiysbdTC5YwWsjRiKdOMXk5Qt59ZIRLNrzpgQwNPNpUdYnhvDjKpIKNWPLyes1D1Vo9Ft3ExXbWxK7BaL31mLcWuzBS/abNk/afiBeAMQrit/MrGEX5TizIMtvyybvZb3sZjDMyAtL1qFIsjNjTUPDJlSeOHQBO/rYvMZX0WN9WDNmChFyw3UNbEIl660H2fy6NxvwBa88TLuPC7D3Tmfh1x9hlBRsQmXYjXdhWLeTQ3dmsvyxt7zYgIf2fkbM+fkDbrxtPH98/pHH+YbGZxK/qhmzE5d7sQS7t1EfDeZFyuUnqW4dw7ikf8g212CUTIyILIdIvcrWYbWGF/dnYku0+Cd+O03Ezs7lXyX3sWT2LL8pd4oko+cs6f/+d/gNgKsGROQVezy4vMTATH6d90njOiJJ3gIXPATVhj3tIQ2GR1Tw6ECFlMUmr9TXba8m88HEFGIXr3IdXzcIRVU18yu70j98N3JyIuq2QhqCFBZGouE4oJBoPsbG8A5e7AnuuCDMznRbGFqVWzVVH0LaXKZPbDM7LMUw8y+0ma59JIOBI98lEXd/KfvOr/WbhSVsVjb3kWieWMqJXxKJGrbf535p4/PgOt/9XTa2P9KezX6vxxckg5HDY/qw59rpONgL1o1MA2mvV19vTxzEkQcG0DZnP31ii1DqqtU/29pVJ+KlMn3sqEJmzfFEDn/fkTbTVvJbYFYnQE/H7fDiygb3a/1xPkve19OiE97KY8frKkgScSs11mqdSdu0BbWqmpdTeoK8C4SmZ5rJJc42tM2FtNzqetdVcxb00t+PZjOiabN8M6KmBqEJn0pHkkEXOgOfeICYT72zHXXs8fN7w3AoS66augpmyciOPt7ZZEJVybxiGxZhR3Or/ucQjL4KnHf4pghex2P7Kc2OI5VWElCpWTBKMjah4axZr9Xth35M8PzDjUeDQrc6I4GTg2uYuuNiru+jF/d2L0K8xtKOSYmLeWraPZR/1YqYW8rBrRSfr9Jovmp1+trPfUAbf19P97/vZOdFn3rtVx+ratUGhZUwNJ4uWxuUiTywnObXbPO7T9hePS/fKCloCbVIJqOH0DUkdeSqzA3sejQK1YegEhUn+flQT65O3UZNxxaY/J/KBbOJtkoNEEWCsRzJnAoBhK4iyQ2WbvRo3jHRub2kxY/0I/HOHeyeHs+mgfOdY2JofKaz/KfjOduEStdlmaSP2s++J31UPGsgK1Kya0GnxTqbtFlZ8dgUwOQs9Vm/RoU72s5ag5iuko+bRuVWoXRVL3et7xBtlaNBk1gGI3DBs4Sp++fSHCtje/3NL/9cStgvdXU03O9Hvc/usjRqvz4ObEIlfNmWgJMx6AIJYMVL0zC+rD/TcSX92d339KrDSQYDzZdF+9w2NGc0kmGr90pJU8ndnErvrRPAjdFj17/eR5FkMhZ7M2akH3AVl3Kw+kqqRPKmGoTNjrG0nKzvH9bbExJdSw+i2uy02lTDoO8ecdZo2Pe/IqYEiNhUQti6TpzqaIY+3tuviaxk9P4LyX4yj63D22I/5p8i57SgqSSP3oZln63BAhOfHRsI4syXECweb6fNR80C7tPhtyoYq3/unngQe5gZqtwqXNns2IXis1I+6GSgh37qSNyjEZRcaCBpccP9EkYD8QZd2LdTKpDMZ4hypB7cTTpCgsqBKWwaOMNjEvYFo6Sw6+LZXJZxJ/LZoSjzRB2zR4RcgCo07KhkTnmABEOe37qsDRY2r197uRGTgJKegrqz4aLwPiFJpEzX+KLvUNotygs6v9957qM6951F2BokZAW4qYOrTOTOmf38sqM0FsJu55vkPzx+q9Rqyc6/k/hVG/1OYJ3Hb/T6TdkrU61Z6Xy/d5Eh93bSH3Upd8JmR2gq9v3FdHnaVQvbXlkFmoq8YiNpeW7i8CwK3QYdafbSg3T4aBuXn7/RL51FTi9wGiEAACAASURBVKt1HLdGorVugZLmTWh3piBsVjYE8dKundqr4Z2agIjfozjU32UT9AUpd4OzUMespG+RoiI9tttLSvmrKAXi2/o6HIC499ZwUK3mqZx5QfXLkhjrnIjaKjVgbBotUmPQ/uWV/Dl9ZoMC1wFVaMybM42EV4PT+k4LQiNp3mEswoYiyZglI/GvrfQrcM82tGbBU854QQhq/3uS9yZO49itfRtV9hFAnNIpq+T/oc9cDgvD8msnL3NGlBxG/LVb/RylQ1gsXn8AD5Ve5Cx4X3+br2PRdFONktKR7dNS2TE9he3TUlFSOur3VFP9tnOm0eCT2Pdcf6oGpdErqsinDahas/LY+mvZ+Wp3pIPH4Lh/2u4zgeNqYMrvAouFFnP82aJOD61m5ZLYv9gnC7A7pn5/JQBtlAiKc+pVShKCFnOj4D3//G3CZuXSzx5ldPMyjt/ZcEH4411d/WmrmBDmsy90geDYOdz2bRJTQVMgBNMSVjsdryMKrzg35/XXnbVbGt7JHySJZvfaefLBMcTOzm10EXat0m2VJf8PBK8kodXWsqzHj17jpfNH97nq+QYDWcF2mc40vvynXk6KpuD7ImNv05y1F09jw+AZrL14GvY2zb1KQp5tNHi2lDlHsETLfHmgHzXCW83cb7fzUZ9PidpdwbbnkxBWG0rzs2eGPmIPvLy/vs6Oc7YgPxnDgQm9A+6T+qHu2FAkmY7XeNsRo+atJi78JGX3+heoqR+WsM1aze2P/NKgdnMyxTUZRsgmbO18287+f4BkNFH03wFYhA1VaJglI/ZrLI3WEM9MZySQJPa8lu383GgIwXk/7ueXWe9S/PiAgAXJfR5exzen/C+uH0AIWq6IoVpzyQ5VaBxRq+j0dK5eBjZISIrC3pEye22VdPrmcGBGCX/dkSBCNhIumYiQjTTCtXHG0KDQtcU1J/qOYvq22u9RqNiB/fYYOhiqUSN1bUtu3RKt5szbUx2oUCP9bltrsZL2ZNO4qoKFYd9hrNEiYEFs7UgZeRbdG/tw+1997rPrhW5Yrjjptx1RU8uiyh70Dd/TIAOGiPX0/Na0DayJ/78MYbMy4uqVyMhO9gq1vPy0aHqa3hm9EH3ypFzn56Zg/YXR3HDpLbR/eWWjHYoIUUe39b8zL3yZ9CdmyTXONQQvHB4cNL2NA0JVyehygPmVPdD27PfrFwkIScIsGZ1mp//FZNzgkzBuLWb3ug58vyaLdKO3wLsiwsK/t46mxevFpN+Xj33v/rPCtupAsTXG77bWirVRM2dTYD98hNgtAuvg8/zuI2x27ts8CoDB4RpV13l77MMW5GPfFM2pHB/eSUA7UcGCQz2IVyxUd24TsE89kzyJQE+kNY2vq7FYUav5jXWuD1VobLMG9pyfKbzatgCjpBAhm7h821VNZ4w4QwjGRBQIA1cc5YelX1H8ROM1XQA7KkZJQYoIP7dCRlbY+WEfKrQap2lBFRpGSWFHH1vj5YSm8mHyd0z9/QrdPt/YSUxTkZevZ2hCL+efvHx94ycyNwTNSONxTAOQwsykf3CE5BT/dObvdf4Kk6yitGrl5DY6W1haku53W6IhisKpQZIwNhVCEPPDRoovMyH5se0KVeVEoes+HLzGx+ASgo7/zaX0Eo0DT9V5jGWXk07Y7ByviiBWNlGZ4Gm7kgwGJKPJ+Xdz3CqP7VUpNo/tTXlRG8LO2Vm8dMGVQdt1FUnmP0NupfCTrDPeFwckg4G9L2djETZsQuXVY2kYb6hq0jL0TKLVl/6ZYRuEJJE7PJWhd4+j/UtN0HRxceKpyfFNJtBsNGQFQ3w79g770MMBr0gy75Z3bLSW60AbJZLOH1cEta9kNjv/nO+AJCGZTM4/5yQkK57710Er2MotqZdgWLqWoQm9+Fdyf/6VNpArOvYDSaK4fyVXJHnKnCsS+wRkEW44TrdbOyrjjdzf/jsswjtcq0ytYmLhKIoK29JFK0RUnV1t5mRVYMP7zGEf8+b47gH3OV1o1dWkvrKVoq9SScjZ4pGRBoDQCDvqEkaD0/zEDAtB+n35yOd1Zd+8DGK/iaBF/kHs+4owtG3NdUm6xqbWG5+7X+xLQi8XtcwNUXke2z+85GOeX3CV8/upb+No9b6nc1GWTm+53WG+ghrfksyXx1Hw+PQG9z/v9XHER5yg3SIjjD6tU/uFsNvZedsMVKFnLP51Yy/UY8GRpEpGU2DhXG/ikhQlaE1NCg+Hpq7AhED53M57Hady6ysP0/r9vCZrZlWJETTb6J/O/IxCU3lu+Q/YhOIR4fLFqZb80j0GaODeyQpLin0Tbi5a9JXXbytqNZ5L9vS1OFh9JVUi+XsLyqotKO3j2DYxzhWnO+UgavFB1P7d2ZNj9sml5rCLOzgLdz/Xi/dyPmRzbQdUJJrJR/mhm4u3r6FJvkGha6hVqW1l4pWNQ7l+wAdOoesIOP+rJo6JSb/z3osjKf6oLe1Hl6CEu2Y2X4kQvhDsfg/0+Cvg9iERNu5/LZvkyauabEOT3NhL/S3H1IqTJORsYf+zA0j+4rBX4H34EVcbj7TzbdcFQAi0DdvodFs4JfdlcmhgPKkZGvsrook+ZaHLH4NJnekpMF/PmcOQcPd4aE+pPDjMRna3b5zfh+Tc5OTQcsCq1j16WfHWfoTm88XUbYM6IhYUsPON3qTOrWJo+ywPQeAQRs7ZXlaIyzrFjnuakT5xHT7R0LK3/sRWH7KClNUN3Ljv1K07kQzBCZnD9/ahTU4RfVvud2akuaP/el1oqkIm/1hHDn+XSJvpwYW/nRqcTsQPqxvcz6OvsiuW2zqxJbf1eZjWH7qtaBzbhfBeyciKi6HabSVycIBE8z8jwGpzbheaaNQE4gsOE5P7mufErdmkGnMxSp5jc073JKDh51E7LAsIwHLshmrNyq3/jCXduNnjOhysvqc0O8MKJ9FurQFbfAxrc95yZqTd+M04lMNHKcsI55/rXqOZ7BCJj7hO4DbuhN1OyhP5vPL7bfzw8btEy+HeWm0DcqfhNOAaO7IdUtuUUStUHAFbW2xWjGgMCKvk+cOXsXukiS6TTqJVV5/VmTRC1mPoHDUYQF+yuNde2DpqGld/MDJgBlIg/PbVbOfnbjPG+d6p7sZ2fGYllkuzOHhjNhGlgra/HgCbjfjRe519Sm4oblYIJmxcywuTM4h7U385EyjmGBBzl8SS0gLmntJt2fZLsrgmsoD6gtYdiiQT4TbYH09dyFRcVN9D4zPZ+3Iyt2/cR8HJ9qzNS6P5bhnNAFWJGmFJp/i694d0NxVgEyoaGgYUdttrnK0Im5X0R9Yhsrpw5aYyFvZN8JntJIeFMTDvBP/cKUifuN73yy1JlEzKBnwvw08+W03MrbGoZce8NzqEtaYy8rPfnP3NeO8BEo11RJEN4JOif4iQcwmT6r8Oruf2RCs3paD1OmqfsHPqseA0zmAELsCeF/ryzY1TmJx0PjunZTFjyKe8ndqVoiclHusxj1kV19NiRRFfr/6eEXc+gPlwFd8t+JRrr7/box2lczK1HaKpSDJS3k0QLunCevdNM0kKu5cuM09iaxnBsa5hGIcfpWJ9K58Ep/5w/M5s8l/Q6y2oYp3TYem4W5LBwOpXZwAu5UsVGnNOtQtaNhy4we5zZe0LZslAwg9GL8eaq3ZErXNGEBJEyWZn7QVn9IKMkw04KGhNXyk27GFYs5W49QrHD/Zm9O8jsHVNxHjkFKVD2qCZoNUGC+Z/tpBWuxpatUQymc6q0P2mazvmZV3KjjERTB60kKGRO4hVFDZYw3nrwFC2rkgm4S8bpsI1gRvSdBZPf6jSBPfu+jcdnl8Jzz7kdz8Aw9J1dPhDfwiiZSw7n0jnkdY/s9NWy7MlV3LynlYsbqB0wNupXYnuUMLxm/vz+LOfMSJSF2DvnThG8rdj6PLUNm4+AdWPNj4OenhELW8Pdlt6SRLJT+fz1xMRII6RHlZF+XWZVF53ise7/kEn01E6GiTWWqwsOZXBh38Ppst7x1G3FXqkZgubFfK28Ev3GLRB6bR8tYjBMTsw1rEB/1HWhcrJ8SzvuQHkbV7L4iPzu7A86xOi5DBUsRZ/Loa/e36DcaM+wY4v7euRp1/6fVeGJG7HJgzcFX0IvfaGwpCcPJQcjeYGC6sGtUSrrPSrgdyeOIids7P4evBM+vmJcXZ/+fMsNm7880HS71zjvB9V15+PbBdELChA6RDPnlviSXxhNZIsUTS3C4k3bQfq0qe/O4S2rxjrRRlUxbkmx9QPD3F9h7GksJ70z2q5j9tIJ482H4fzwgUjSflpPfbaWq7dMRLjr2vQgGt35MAqV9bW3leyeSHnS7LMJbRSHEV8XOfYfvV7mK8xogqNGmFlflUCz6+9wec1OzA9YRWUuv+iT0AvlXXmr54uweq4Fztm9CJpQS86/gDmhfkB2/aHOYM+dCpVDUGRZCJ/WX/ObPcHv01jSOI6HjwwlOYGCwtLcun+4QN0et5/xqM7QmzAIYQQQgjnEKF6uiGEEEII5xAhoRtCCCGEcA4RErohhBBCCOcQIaEbQgghhHAOERK6IYQQQgjnECGhG0IIIYRwDhESuiGEEEII5xANsgH726a0aol6/AQl33bh694f0sVoZqetlnFjxmNasqZBttZg0n4bYhd9a18uXYx6cQoNodcpqMsCM0oKwy4dyeItL/ltY9ecXmy95H1n4LtNqMi40lEtws7AlyayYdpDQTO+Fj0zgMTnV4OmsqS0wJki2CB7LThTXXe/nk3KZD3H/pviXG5on+1sI9DxYsB5FI42s3fE+6yo1Xjpgiuxl7ii2oPpg4PfbOjmkyzJqKvLKwRLSgsY1uVCFp/4yIOttfby8+j5XIGesCArnLyxL5ZoibByQbN5+ciREdyYv4Ovb7oMsXmnbzZgNxx4agBbx3nXcrji6lsR+S56lqDup6yApnLDtkOMbl6CUVL051GPDbh+G5LZzI73Mlg99B2i/bDQmiUjl918J0uXPn7G2ICb0kZavpnN/6cnfy+YFLCNmmv6cayrAVmFKWNmMTjMRvq8cTTbLWOuELT4LNdrfDne0YP2SvbYIyi0tiP3ZAol1S3Q7o1A3bXPOc4h8Pu6pLTAWTrAH4bGZ57WvVhSWkDPN8ax+Y3g31fQx/zhMX18sgHbDqYI9z4Pjc90Xu/Q+Ewsw/ry2cy3GT32IcJ+XY+w23l890aywyyEx+1rGhuwX9jt0KcbYzr/TXdTOKrQaCZrmMotyD27NHz8GcDDnbKdD9NRVMPFCkyD5JSpowsYIfrqX2SFwql92JPjogFXaKAegDvqBKZqFkiyhBASjx7qhTq4F8rfG4Jroy5RRW2mgqYiGQzkjH6AoldNpDzVcB66obyaqD16+c3OxhpOnt+BiO9LGziqHupy8ivUcCSDJ6nmz9uWOT9LBgNFX6WTNOkoG5/OZFnpBwBYxCpUIbAIO32uvJ+0u7cyt0citovCOfBcX5KfCXwdbdb7Ttt1F7jBQpIlhAZ3RR/CIjRu2XcpcKLB3HhhsZB+9xoufuJRDP3KPbZFh9fyZ4/vAJg7513g8Ub3yx0ljw1oeKcAWJibSdcC3wzL7oj4ZR0RC2SkMDNjW91L4S0zSJ24ylntK9AduSvrWkR8K7QN25AMNuConv0VZGLVlH0rUUWYX4FbqdWS8fN40snzub0x6PD1PnijCQf6YQNusIqeDxEhS4HrFzdZ6EphYVhahfFB4QDu6lOIWTKwsCqdff+R6HTTjgaP91f6LNjCNwDIClcMH8XT382hn1kNmq/LiXqDJmWeFXIa10T9tiShF8WQDAYKJmTQY8pGtmU1Ij1RVmi9Sr8OYbdjWLqWws8LGDrZf6k4J8pOEL1bLynZSonkcF+ZpO8bfSVB4dC4fiRN2MfWZxLYPtxFc64XhsZJeY2mV2YqGmKiwx821AFntwKcO4TdTtmYbGxiLWbJyPG724B8KugqXR1e8xYCQhMM03qzpLSAGD+cgY1Bwisr4aXAaeaB8OTl83k//9oG93OmpwqN5rvqfpSkgIVuhvcbDgaF6/7O467oQzrTs8nUIKNwfUzsNMB5PiU9hYV/fuuxPUoOI/2+vKbX+pUkDEkdgQKPlV2wEKp6TtmAm2zTtR8pw1RuZWO/r4iQTSiSTHdzMVFLI89dlX5NRazfwhMTxnqYBf5XkAwGYjfX1WCw25GXr2dKXAM1IOpDU4n5JBepV3cnxcvFW67G0D6hwUPVsjKab3fVZhhymZ+KXk2E+6xf01ZgSW/HruGzgipKUnjrDMz/bOXApadB0tgY1L2Inz/+pnNsqFt3NqosorDbvf7cj28MR5w/GJI7ndbxb3+aQ+z33oy5/iBUlbYr9Ap1hnZtAwo6e0kp6sHDVGtniIlECCYumO/xk02oJC25S+dKa2pVQIORfTfGN71fmoqyagudn9lKl6e20eWpbU1vKwg0edRIRgNS7gaS5411/jYwTMYWee6FX/iidWfkBThdCLud5l+t0mfdusF80F6JGOCfZcIfdtwfrpflk2TE223Y/nJg9gi9AwKpupa9Np30clrC6joN4Myj05O5/DRnZqOOuTzvEB3/z9khDfUF8bGNrqYILMJO99xRZ6WY++lCa356ZJ1fj32T0nsaMb6EQN2ir0QPjUgKXNRciNOqpuUBSULpmsaQCJsH24hRUki/Y+1pMb4IVaXf1Zv4prKJ3ICygvWiDLa/q7ME75ie0uS+BHW6ph4omUycurE/112w2uMmxuy0N47h8wzgf0Wt7Q97bk3QmUolicvXjKHwHjcrjrtmEUAImA4bda1KUwn7dT2/XvhucCe32dlha+n8uu3hdo3tftBwrHCCxcOxTSu12SQIwcLOC7EIGxGyiY53Ffvn1GoqaeQZgFYQmII8ICSJMY9MpN2UptHa11x+6twx4QrBSws/93KmDZw49vTvvabybvvfeGzl9U1ipJAUhfJ0MysunsqGwTPYMHjG6fWnATT9jmsaUUU1mGU7GgKbUHn2aDeKrhIes5ZkMDj/zhVUobHT5r9sY2Mg/MnFAANF0vSyh5LBiH1zc14e4DKsVow6n+prz+fIuAHUDs/yfV8kCdniohGxD+rJITU4jUhYrGy3xDm/z/zXx35phf6fhaygXqyXsnSUB1RPVPhdvu7+4jz6F5w9Xr9A2PuyG3+aJDVOGxeCP9+Zzp5X6nGwBSnEnsxYdFb5DB2QjLqClmk2e3ClFdsrifqm6WQD7oiSw0icJzeNrBJAhghZIVwyES41jUqoEadqGiRFwXCskq+WDkRGwigpXNZsM207lHs8dA972DlEgeU0bDx1MEtGYnd4Dko5IqJhinnHGBIa1mjBgPADzk2V7WUif15Lm+kruf6VJfp98fGiaaY6x5yiUHyJmS+OBenl1lTK7S4C0ZZKFXJiw/bg/5cgKQp7rjVSrVnREEwp7xRw/12DP+HZ1ltO76RNNF0kPV7P3NJIoTFi2C0kP+bWhqygtGrFoQmBx8tuWyVDI4oada6mQthtHL+uikrNpYxVCgtDZ0w6o+eJ3NI0WnYAIUO0HK5HP51l7b9xrbsJU/XkSU70bs2/LtCr/atCY5+tNSdXtPGYuZaUFjj/zhbkCE8t0I7K27svOyNtG391OcLKb8tGq65GPXmS3Z83HE0g7HYuOn8Ly2tcdtX45S5mjV+6xzBgg1W3qznurSQhZ3QmuhCnd3n73TPY/nhwXn9htbG32mVeSDbYOZEVhD24CThor0QVGjahYhOqFzOwEJ4a16vH0s5KP+pD2KzsuX4WZsmAUVL45YFLzro9V6eXaTxOjHZpqW1XNuP/7A6OosaBRYu+Ys9rrjZE/x4s3PAbGyZPR0lL9qv1/lGdTivFm937jEOSMMTHsX3QHGcMrE2oHFdVnVL+TLRf54y07ytqktYsbFbaTl3pwRJ8OtBEYLEa9JpfMptRYlpgP3TYyfTZ7Jt8Flzam2kJdRQzhnLarbacVod9n1zSeZz8aMvW/l0B1wM0oGD7tg2S0aVhevFP+fJiN6BkRB52nT9l1Hpe37eKRzv1d/F3OWJ1w10PfnbiclK+GcutdfJGWrmB8tuzifl0FUgyK3uFo7RuwbEhKdjDIaJMI+KHPGK3GRFC8OyetUw+nIXxd/8vo4cdS9MoqXIJ3RglgsP9odncescEyR3mDneOtML3zueOq7uzeMEXvHC0G2GyjUGRO+hv1m12Ghponi/80p5RFE7t16hzBgMnqaSm6g6baH0lYkdFQUb588xGcfhC2MLGCUsHYr5aC5/on8uuMvJCwiiQtgcnPCSJQQ+OIfm7XCel122zfwHgio79EOp+v+1UqA2Hu+ms0wZkH5xxwcAxxhbkL3T+Vq1ZiZBNjOs6BKSa0zctCMG2/7T1fX43s5qw2Z3jQzK53hdhtTp55iSjtzj0Fdrq/E2SMC/I557EQZjJdy5wX07pCeDBsuKOwEK3TjgduX8A3096jUJbDBM+u4eYnRotNh6n5dfN2dTxXSAMVWjEKrWE7S9HijuzzpudH/emY8Ixwq854owRlIwmJ4Gi4cnDHnxKiiTT8qNcj4Dvgw/0I+eOZVg0A7+9m03LOWtdLyp6NMapjq6HpAqNcq3Gox+mxfns/KAv6ffoFCSPdupP4Xvn02XacURRKSR1oGhELJ2eWqlnNU3tCRSQOnEVjK9rRAhiPl2FoX0Cu8Z2IGXKLtTDR2gx54i+XZKQenfD9uop/uj2E8m/30PaaJfQcOencuDCTZ5xmpM7LfL4fscly1iOp3OzZF46BllDqscKbNdkVFVm1XlbAKuHplQjrE4erNarZORqCylzx5L68CqQw1laF79qEypRchhpt69FSBJyWBhZz95H2PUanT+sgLoYSMlg0B05QnMG2xtP2r2cLfU16PrY81wWSfOrYPUmkGQOfhrnHA898/5NnBw4VMzjfMFoxHVtuU9CkqIgmsDSK4e7nkvR++24OXWNBwVOQAjBl2+/ybA01zI9zXQIMDZoq300djcH7ZXeGyRJdwILDculmQhZorXBh+mlbj9f55EMBoQmULMz+O3r2R7314ZK5ivjaGdbE1DgSkYTi/cHmyxRwLIab+2ySWzAMig1wdnE977Yn3Wj3+b3mlbMSEsNsq8NCF1D29aUXZbEt5Neo70hnHhDNcvveR0NnX9eRxg2ofLW8S7MzLuIlPYayrK9zjbcZ4pgTAzu+ztnihqFJd3nYd6lv/LfVEYzeemNdJwvuPiVFTzT+hccwfnVmpWMb8eTKud7vGjhQ44wPnYNEbKRZ59fz+U3XUfkXXbsB4pBVpA7deD3V6agClOdpia45O1HiTd7ajDp9+RTOPV80savRjKaSHsgD9XByLp5Ox12mBDArpd6sWn4FJLmjyddrqcFCYG9uIROTxYjtU9g95v92fXvmQzbMYz/JC5hwgfZJDwhMzQvkzTZM5tt2IS/vYTS4u5fe+xjlgy4W46earWdyy+4w/n9iwMrGNWBOq+992CVFAWMJp3UUQi+K15FpQa9P3+IXXXvd8zctWx7P4Oub5Sj1aUO139+DoEtVI2oUpVj3Qw0+9EVwXDyuj5ot5VxpLAVXd8oxn6gGHn5en6sasF1USed+9Wf/Nzx0K5tDApbRdRtYfq5hcr6vnOp1gRDt19J3DUNx1yWazW0UiL5pWQtQ7bmcLLWv+MxNqKGxV3mAzLlWg0Ow43I6uLBVRYsqi5yZW8mPm5hSfpFhMlrXWPXMQk4Jp66ScohsK6cMon2U/PgJX3z2NfG8/eTb1PyfXcScrY425BkyTmxtc1tjkXYuOWOCRjkAk8m5+ye7L4uHM0kKMyZwb0HLmTW2OuZHiZjJh+tpgbJYKDy6ixKhqnIJhV3BmaA3S/2ZfMtU5HJAxQUSXZOgje0z6YtKwNmwAFUjugFQWaoVWtW7lz8IOnG9afNBmyUJC577VG/55IMBqfduMMfVqJuD+OayEoaE+8QUOjaDx6ixZxDPLDxHspeVPmsxye0UyRaKboNtUKrIVoOZ60FZq64mK6PF6KWlwdqsknoPH4dOc9dya1/5TEy6hhXR5Zx+fApmK80oKFhE0ZnNtqwMfeTuijfS7OJHraLF9YO4rV2a1AkmV+6zSVqtUvLUEU+GkanMBv4xAPEfZaLLw65tPGrqVmSRMXCOBK+2IF6/ARKSkdsbZuza5SJvVe/DxSQ+uVE0h9d7XtGd6T8HjpM/PIEhv4nE8lwhNfsGWwunc7QV+qEV73r+HtSNspsT80jwk99AP26NC7adD2Ry11Mu7dcO4ads8Lp+uZx775JElqzMI70a8719y3liVY7UIWJzLxbSHosF+qErrBZ6XzvJiwXZnDhl5Xk9onyqfUozZoxYtVufhqRQPiiAx4mDWO1xu/nfU2W9RYwuDTMp+fcwoix7zqf6cDPHqETvuN7395/OVd0/UUXuLLCzhm9qdRWsd8u4NJi/6YkN9x423j++PwjNDQWd/s24L6gE4gryNx064MsXVr34+rGpykDhC1wTcjqzt2E794HssTi4gKuSDqf2p/j2L+vNd2eK6XNtyf5Z1kPUudW8PFP73N74iDip+Z5OI9az8ilZ/Y4Ci/9EKVU5tdqIx8fvoA9J1rydtevGRimj++eb0wg/q88L+38WEYEO256j6suuYFhE/qCVomBtRjNZoqeGsDzoz/ngC2Wq6LeJMUYxcAJYxh6WxZoqlNJSp91EPOtRizCBnXRI10W3Uf63cEnCh0ZWRM0G7BRUujy5HbUeszPjWUDViSJ1ZaWtH13JbzjO0vw8HepjExaz/L+LTEXVwR9Pe4IyqarbdxO7JWCiegeUUP7BCozE7BFyjT/cT2SwUBnyzrUs8TGKex27IePMLtLJ2aLjlTc0h9p1FGizbXs2JlA5F4DHaZvQjt1ijDDer/LvM19JYZpvREDMzlweQS2tBoiImuprjajnjDRzSroywAAAkBJREFUao1C6/k7UI8dJ9aQ71PgOhD+ryKiYk9y8KPWzO65kK6mVWy2CqYdvpTMV8bRdupKUg1rArbhuLbwn9Zy9L5s1j09A5tQyZjyIPFSrk9hbfptPRfdey9zp79FnCGqwXv3YOkAoq4u8dQsCraTPsbOgUkD+PH+10gxerdTrlYzv6oTyfPGkv6fNcRr3qndwmbF+NcGVp5nwnZZBu2fL2RQi10oaNQKIwsOZyAmRvNDt5MgF3kJv7AFa7kyIYvY4VEUvmQis0MlA2J283XRYTp/ez/tcqH5d+voZF/l/wIvLSbzwXEMXb+S7uH7KbJWccXECUR+W0d7HsSS37BML+bD/Ci+SvuO6ADpvRVaDaN25aBeVYlS5bYKcXtWQzefZEmP5mgX6Q4Z+a/1GNq1pWxIMi0+zyN+ZQSl/U95908I56R08ZarMVn2U/1lHCn7LNiLS1g3dwCpy06gFWzl8jVjSGCLT7t8+p0bGaZmceCJbIbk5PFEwkJadLDz1tHB3LKuF+n35BMnr/b5nghJLx5VP1VXx+o6ivtjjEgYBEAU3vTyalExmS+Po/mVBwl7tjnSyg2ky+u99guEb/u/j1lqON7fJlRG7hqGerKs6fZhTUO2Coa89qgucP1Bkoi//xTH5kfy3c5lKJLEE4ezWHd3Bsjbg852DLEBhxBCCCGcQ/zvc2dDCCGEEP4/QkjohhBCCCGcQ4SEbgghhBDCOURI6IYQQgghnEOEhG4IIYQQwjlESOiGEEIIIZxD/F/DFVgONZy1bAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 200 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_labels = generate_labels(display_sample(pickle_train_datasets, 10, 'Train Datasets'))\n",
    "test_labels = generate_labels(display_sample(pickle_test_datasets, 20, 'Test Datasets'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cYznx5jUwzoO"
   },
   "source": [
    "---\n",
    "Problem 3\n",
    "---------\n",
    "Another check: we expect the data to be balanced across classes. Verify that.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_balance():\n",
    "    fig, ax = plt.subplots(1, 2)\n",
    "    bins = np.arange(train_labels.min(), train_labels.max() + 2)\n",
    "    ax[0].hist(train_labels, bins=bins)\n",
    "    ax[0].set_xticks((bins[:-1] + bins[1:]) / 2, [chr(k) for k in range(ord(\"A\"), ord(\"J\") + 1)])\n",
    "    ax[0].set_title(\"Training data\")\n",
    "    \n",
    "    bins = np.arange(test_labels.min(), test_labels.max() + 2)\n",
    "    ax[1].hist(test_labels, bins=bins)\n",
    "    ax[1].set_xticks((bins[:-1] + bins[1:]) / 2, [chr(k) for k in range(ord(\"A\"), ord(\"J\") + 1)])\n",
    "    ax[1].set_title(\"Test data\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_balance = plot_balance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(numbers):\n",
    "    return float(sum(numbers)) / max(len(numbers), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_balance(sizes):\n",
    "    mean_val  = mean(sizes)\n",
    "    print('mean of images: ', mean_val)\n",
    "    for i in sizes:\n",
    "        if abs(i - mean_val) > 0.1 * mean_val:\n",
    "            print('Too much or less images')\n",
    "        else:\n",
    "            print('Well balanced ', i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check_balance(display_sample(pickle_train_datasets, 10, 'Train Datasets'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LA7M7K22ynCt"
   },
   "source": [
    "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
    "\n",
    "Also create a validation dataset for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 411281,
     "status": "ok",
     "timestamp": 1444485897869,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "s3mWgZLpyuzq",
    "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (200000, 28, 28) (200000,)\n",
      "Validation: (10000, 28, 28) (10000,)\n",
      "Testing: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "image_size = 28\n",
    "\n",
    "def make_arrays(nb_rows, img_size):\n",
    "    if nb_rows:\n",
    "        dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
    "        labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "    else:\n",
    "        dataset, labels = None, None\n",
    "    return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, train_size, valid_size=0):\n",
    "    num_classes = len(pickle_files)\n",
    "    valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
    "    train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "    vsize_per_class = valid_size // num_classes\n",
    "    tsize_per_class = train_size // num_classes\n",
    "    \n",
    "    start_v, start_t = 0, 0\n",
    "    end_v, end_t = vsize_per_class, tsize_per_class\n",
    "    end_l = vsize_per_class+tsize_per_class\n",
    "    for label, pickle_file in enumerate(pickle_files):       \n",
    "        try:\n",
    "            with open(pickle_file, 'rb') as f:\n",
    "                letter_set = pickle.load(f)\n",
    "                \n",
    "                # let's shuffle the letters to have random validation and training set\n",
    "                np.random.shuffle(letter_set)\n",
    "                if valid_dataset is not None:\n",
    "                    valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "                    valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "                    valid_labels[start_v:end_v] = label\n",
    "                    start_v += vsize_per_class\n",
    "                    end_v += vsize_per_class\n",
    "\n",
    "                train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
    "                train_dataset[start_t:end_t, :, :] = train_letter\n",
    "                train_labels[start_t:end_t] = label\n",
    "                start_t += tsize_per_class\n",
    "                end_t += tsize_per_class\n",
    "        except Exception as e:\n",
    "              print('Unable to process data from', pickle_file, ':', e)\n",
    "              raise\n",
    "\n",
    "    return valid_dataset, valid_labels, train_dataset, train_labels\n",
    "            \n",
    "            \n",
    "train_size = 200000\n",
    "valid_size = 10000\n",
    "test_size = 10000\n",
    "\n",
    "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
    "  pickle_train_datasets, train_size, valid_size)\n",
    "_, _, test_dataset, test_labels = merge_datasets(pickle_test_datasets, test_size)\n",
    "\n",
    "print('Training:', train_dataset.shape, train_labels.shape)\n",
    "print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing:', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GPTCnjIcyuKN"
   },
   "source": [
    "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "6WZ2l2tN2zOL"
   },
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "    permutation = np.random.permutation(labels.shape[0])\n",
    "    shuffled_dataset = dataset[permutation,:,:]\n",
    "    shuffled_labels = labels[permutation]\n",
    "    return shuffled_dataset, shuffled_labels\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "puDUTe6t6USl"
   },
   "source": [
    "---\n",
    "Problem 4\n",
    "---------\n",
    "Convince yourself that the data is still good after shuffling!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_shuffle_dataset(dataset, labels, title=None):\n",
    "    plt.suptitle(title)\n",
    "    \n",
    "    items = random.sample(range(len(labels)), 20)\n",
    "    for i, item in enumerate(items):\n",
    "        plt.subplot(4, 5, i + 1)\n",
    "        plt.axis('off')\n",
    "        plt.title(chr(ord('A') + labels[item]))\n",
    "        plt.imshow(dataset[item])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_shuffle_dataset(train_dataset, train_labels, 'Train dataset shuffled')\n",
    "plot_shuffle_dataset(valid_dataset, valid_labels, 'Valid dataset shuffled')\n",
    "plot_shuffle_dataset(test_dataset, test_labels, 'Test dataset shuffled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tIQJaJuwg5Hw"
   },
   "source": [
    "Finally, let's save the data for later reuse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "QiR_rETzem6C"
   },
   "outputs": [],
   "source": [
    "pickle_file = os.path.join(data_root, 'notMNIST.pickle')\n",
    "\n",
    "try:\n",
    "    f = open(pickle_file, 'wb')\n",
    "    save = {\n",
    "        'train_dataset': train_dataset,\n",
    "        'train_labels': train_labels,\n",
    "        'valid_dataset': valid_dataset,\n",
    "        'valid_labels': valid_labels,\n",
    "        'test_dataset': test_dataset,\n",
    "        'test_labels': test_labels,\n",
    "    }\n",
    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()\n",
    "except Exception as e:\n",
    "    print('Unable to save data to', pickle_file, ':', e)\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 413065,
     "status": "ok",
     "timestamp": 1444485899688,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "hQbLjrW_iT39",
    "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 690800506\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gE_cRAQB33lk"
   },
   "source": [
    "---\n",
    "Problem 5\n",
    "---------\n",
    "\n",
    "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
    "Measure how much overlap there is between training, validation and test samples.\n",
    "\n",
    "Optional questions:\n",
    "- What about near duplicates between datasets? (images that are almost identical)\n",
    "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def extract_overlap(dataset_1, dataset_2):\n",
    "    dataset_hash1 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_1])\n",
    "    dataset_hash2 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_2])\n",
    "    overlap = {}\n",
    "    for i, hash1 in enumerate(dataset_hash1):\n",
    "        duplicates = np.where(dataset_hash2 == hash1)\n",
    "        if len(duplicates[0]):\n",
    "            overlap[i] = duplicates[0]\n",
    "    return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.54 s\n"
     ]
    }
   ],
   "source": [
    "%time overlap_test_train = extract_overlap(test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numbers of overlaps:  23\n"
     ]
    }
   ],
   "source": [
    "print('Numbers of overlaps: ', len(overlap_test_train.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([20, 23, 35, 37, 40, 51, 59, 66, 76, 82, 87, 92, 116, 125, 130, 133, 150, 165, 169, 173, 178, 181, 185])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap_test_train.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_overlap(overlap, source_dataset, target_dataset):\n",
    "    item = random.choice(list(overlap.keys()))\n",
    "    imgs = np.concatenate(([source_dataset[item]], target_dataset[overlap[item][0:7]]))\n",
    "    plt.suptitle(item)\n",
    "    for i, img in enumerate(imgs):\n",
    "        plt.subplot(2, 4, i + 1)\n",
    "        plt.title(item)\n",
    "        plt.axis('off')\n",
    "        plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_overlap(overlap_test_train, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sanetize(dataset_1, dataset_2, labels):\n",
    "    dataset_hash1 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_1])\n",
    "    dataset_hash2 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_2])\n",
    "    overlap = []\n",
    "    for i, hash1 in enumerate(dataset_hash1):\n",
    "        duplicates = np.where(dataset_hash2 == hash1)\n",
    "        if len(duplicates[0]):\n",
    "            overlap.append(i)\n",
    "    return np.delete(dataset_1, overlap, 0), np.delete(labels, overlap, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 44 s\n",
      "Overlapping images removed:  1285\n"
     ]
    }
   ],
   "source": [
    "%time test_dataset_sanit, test_labels_sanit = sanetize(test_dataset, train_dataset, test_labels)\n",
    "print('Overlapping images removed: ', len(test_dataset) - len(test_dataset_sanit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 44.3 s\n",
      "Overlapping images removed:  1015\n"
     ]
    }
   ],
   "source": [
    "%time valid_dataset_sanit, valid_labels_sanit = sanetize(valid_dataset, train_dataset, valid_labels)\n",
    "print('Overlapping images removed: ', len(valid_dataset) - len(valid_dataset_sanit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_sanit_file =  r'D:\\GitHub\\Data\\notMNIST\\notMNIST_sanit.pickle'\n",
    "\n",
    "try:\n",
    "    f = open(pickle_sanit_file, 'wb')\n",
    "    save = {\n",
    "        'train_dataset': train_dataset,\n",
    "        'train_labels': train_labels,\n",
    "        'valid_dataset': valid_dataset_sanit,\n",
    "        'valid_labels': valid_labels_sanit,\n",
    "        'test_dataset': test_dataset_sanit,\n",
    "        'test_labels': test_labels_sanit,\n",
    "    }\n",
    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()\n",
    "except Exception as e:\n",
    "    print('Unable to save data: ', e)\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size:  683578506\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_sanit_file)\n",
    "print('Compressed pickle size: ', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L8oww1s4JMQx"
   },
   "source": [
    "---\n",
    "Problem 6\n",
    "---------\n",
    "\n",
    "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
    "\n",
    "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
    "\n",
    "Optional question: train an off-the-shelf model on all the data!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = LogisticRegression()\n",
    "X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)\n",
    "y_test = test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 99.7 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6022"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 50\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr.predict(X_test)\n",
    "plot_shuffle_dataset(test_dataset, pred_labels, \"50 sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 92.7 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7795"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 100\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.74 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8381"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr.predict(X_test)\n",
    "plot_shuffle_dataset(test_dataset, pred_labels, \"1000 sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 19min 19s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8937"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train all data\n",
    "\n",
    "regr2 = LogisticRegression(solver='sag')\n",
    "sample_size = len(train_dataset)\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "\n",
    "%time regr2.fit(X_train, y_train)\n",
    "regr2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr2.predict(X_test)\n",
    "plot_shuffle_dataset(test_dataset, pred_labels, \"200000 sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
